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Brief Bioinform

2021 Nov 5

Yurko R, Roeder K, Devlin B

2. Functional meta-omics provide critical insights into long- and short-read assemblies.

Brief Bioinform

2021 Nov 5

Galata V, Busi SB, Kunath BJ

3. Molecular design in drug discovery: a comprehensive review of deep generative models.

Brief Bioinform

2021 Nov 5

Cheng Y, Gong Y, Liu Y

4. An integrated computational pipeline for designing high-affinity nanobodies with expanded genetic codes.

Brief Bioinform

2021 Nov 5

Padhi AK, Kumar A, Haruna KI

5. Matrix factorization-based data fusion for the prediction of RNA-binding proteins and alternative splicing event associations during epithelial-mesenchymal transition.

Brief Bioinform

2021 Nov 5

Qiu Y, Ching WK, Zou Q.

6. Comparative analysis of molecular fingerprints in prediction of drug combination effects.

Brief Bioinform

2021 Nov 5

Zagidullin B, Wang Z, Guan Y

7. APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures.

Brief Bioinform

2021 Nov 5

Timmons PB, Hewage CM.

8. Data-driven identification of SARS-CoV-2 subpopulations using PhenoGraph and binary-coded genomic data.

Brief Bioinform

2021 Nov 5

Yang ZK, Pan L, Zhang Y

9. A genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes.

Brief Bioinform

2021 Nov 5

VanOeffelen M, Nguyen M, Aytan-Aktug D

10. Supervised application of internal validation measures to benchmark dimensionality reduction methods in scRNA-seq data.

Brief Bioinform

2021 Nov 5

Koch FC, Sutton GJ, Voineagu I

A typical single-cell RNA sequencing (scRNA-seq) experiment will measure on the order of 20 000 transcripts and thousands, if not millions, of cells. The high dimensionality of such data presents serious complications for traditional data analysis methods and, as such, methods to reduce dimensionality play an integral role in many analysis pipelines. However, few studies have benchmarked the performance of these methods on scRNA-seq data, with existing comparisons assessing performance via downstream analysis accuracy measures, which may confound the interpretation of their results. Here, we present the most comprehensive benchmark of dimensionality reduction methods in scRNA-seq data to date, utilizing over 300 000 compute hours to assess the performance of over 25 000 low-dimension embeddings across 33 dimensionality reduction methods and 55 scRNA-seq datasets. We employ a simple, yet novel, approach, which does not rely on the results of downstream analyses. Internal validation measures (IVMs), traditionally used as an unsupervised method to assess clustering performance, are repurposed to measure how well-formed biological clusters are after dimensionality reduction. Performance was further evaluated over nearly 200 000 000 iterations of DBSCAN, a density-based clustering algorithm, showing that hyperparameter optimization using IVMs as the objective function leads to near-optimal clustering. Methods were also assessed on the extent to which they preserve the global structure of the data, and on their computational memory and time requirements across a large range of sample sizes. Our comprehensive benchmarking analysis provides a valuable resource for researchers and aims to guide best practice for dimensionality reduction in scRNA-seq analyses, and we highlight Latent Dirichlet Allocation and Potential of Heat-diffusion for Affinity-based Transition Embedding as high-performing algorithms.

11. Aperture: alignment-free detection of structural variations and viral integrations in circulating tumor DNA.

Brief Bioinform

2021 Nov 5

Liu H, Yin H, Li G

12. High-performance deep learning pipeline predicts individuals in mixtures of DNA using sequencing data.

Brief Bioinform

2021 Nov 5

Phan NN, Chattopadhyay A, Lee TT

13. A novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe.

Brief Bioinform

2021 Nov 5

Hu F, Wang L, Hu Y

14. A systematic evaluation of the computational tools for lncRNA identification.

Brief Bioinform

2021 Nov 5

Zheng H, Talukder A, Li X

15. Selecting gene features for unsupervised analysis of single-cell gene expression data.

Brief Bioinform

2021 Nov 5

Sheng J, Li WV.

16. Circular RNAs and complex diseases: from experimental results to computational models.

Brief Bioinform

2021 Nov 5

Wang CC, Han CD, Zhao Q

17. Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology.

Brief Bioinform

2021 Nov 5

Thind AS, Monga I, Thakur PK

18. Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network.

Brief Bioinform

2021 Nov 5

Nguyen TT, Nguyen DK, Ou YY.

19. High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method.

Brief Bioinform

2021 Nov 5

Zhang Y, Wang Z, Zeng Y

20. Discovering common pathogenetic processes between COVID-19 and diabetes mellitus by differential gene expression pattern analysis.

Brief Bioinform

2021 Nov 5

Rahman MR, Islam T, Shahjaman M

21. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data.

Brief Bioinform

2021 Nov 5

Maeser D, Gruener RF, Huang RS.

22. QuantifyPoly(A): reshaping alternative polyadenylation landscapes of eukaryotes with weighted density peak clustering.

Brief Bioinform

2021 Nov 5

Ye C, Zhao D, Ye W

23. Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx.

Brief Bioinform

2021 Nov 5

Ko S, Li GX, Choi H

24. TransRef enables accurate transcriptome assembly by redefining accurate neo-splicing graphs.

Brief Bioinform

2021 Nov 5

Yu T, Han R, Fang Z

25. Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals.

Brief Bioinform

2021 Nov 5

Xing J, Paithankar S, Liu K

26. Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches.

Brief Bioinform

2021 Nov 5

Onisiforou A, Spyrou GM.

27. Porpoise: a new approach for accurate prediction of RNA pseudouridine sites.

Brief Bioinform

2021 Nov 5

Li F, Guo X, Jin P

28. DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach.

Brief Bioinform

2021 Nov 5

Lv H, Dao FY, Zulfiqar H

29. EcTracker: Tracking and elucidating ectopic expression leveraging large-scale scRNA-seq studies.

Brief Bioinform

2021 Nov 5

Gautam V, Mittal A, Kalra S

30. Evaluation of in silico tools for the prediction of protein and peptide aggregation on diverse datasets.

Brief Bioinform

2021 Nov 5

Prabakaran R, Rawat P, Kumar S

31. Computational design of ultrashort peptide inhibitors of the receptor-binding domain of the SARS-CoV-2 S protein.

Brief Bioinform

2021 Nov 5

Pei P, Qin H, Chen J

32. Machine-learning scoring functions trained on complexes dissimilar to the test set already outperform classical counterparts on a blind benchmark.

Brief Bioinform

2021 Nov 5

Li H, Lu G, Sze KH

33. Human host status inference from temporal microbiome changes via recurrent neural networks.

Brief Bioinform

2021 Nov 5

Chen X, Liu L, Zhang W

34. Distinct effect of prenatal and postnatal brain expression across 20 brain disorders and anthropometric social traits: a systematic study of spatiotemporal modularity.

Brief Bioinform

2021 Nov 5

Jia P, Manuel AM, Fernandes BS

35. A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2.

Brief Bioinform

2021 Nov 5

Wang S, Sun Q, Xu Y

36. Transcriptomic studies revealed pathophysiological impact of COVID-19 to predominant health conditions.

Brief Bioinform

2021 Nov 5

Nain Z, Barman SK, Sheam MM

37. A new approach to decode DNA methylome and genomic variants simultaneously from double strand bisulfite sequencing.

Brief Bioinform

2021 Nov 5

Liang J, Zhang K, Yang J

38. SSG-LUGIA: Single Sequence based Genome Level Unsupervised Genomic Island Prediction Algorithm.

Brief Bioinform

2021 Nov 5

Ibtehaz N, Ahmed I, Ahmed MS

BACKGROUND: Genomic Islands (GIs) are clusters of genes that are mobilized through horizontal gene transfer. GIs play a pivotal role in bacterial evolution as a mechanism of diversification and adaptation to different niches. Therefore, identification and characterization of GIs in bacterial genomes is important for understanding bacterial evolution. However, quantifying GIs is inherently difficult, and the existing methods suffer from low prediction accuracy and precision-recall trade-off. Moreover, several of them are supervised in nature, and thus, their applications to newly sequenced genomes are riddled with their dependency on the functional annotation of existing genomes. RESULTS: We present SSG-LUGIA, a completely automated and unsupervised approach for identifying GIs and horizontally transferred genes. SSG-LUGIA is a novel method based on unsupervised anomaly detection technique, accompanied by further refinement using cues from signal processing literature. SSG-LUGIA leverages the atypical compositional biases of the alien genes to localize GIs in prokaryotic genomes. SSG-LUGIA was assessed on a large benchmark dataset `IslandPick' and on a set of 15 well-studied genomes in the literature and followed by a thorough analysis on the well-understood Salmonella typhi CT18 genome. Furthermore, the efficacy of SSG-LUGIA in identifying horizontally transferred genes was evaluated on two additional bacterial genomes, namely, those of Corynebacterium diphtheria NCTC13129 and Pseudomonas aeruginosa LESB58. SSG-LUGIA was examined on draft genomes and was demonstrated to be efficient as an ensemble method. CONCLUSIONS: Our results indicate that SSG-LUGIA achieved superior performance in comparison to frequently used existing methods. Importantly, it yielded a better trade-off between precision and recall than the existing methods. Its nondependency on the functional annotation of genomes makes it suitable for analyzing newly sequenced, yet uncharacterized genomes. Thus, our study is a significant advance in identification of GIs and horizontally transferred genes. SSG-LUGIA is available as an open source software at https://nibtehaz.github.io/SSG-LUGIA/.

39. TSSFinder-fast and accurate ab initio prediction of the core promoter in eukaryotic genomes.

Brief Bioinform

2021 Nov 5

de Medeiros Oliveira M, Bonadio I, Lie de Melo A

40. A novel antibacterial peptide recognition algorithm based on BERT.

Brief Bioinform

2021 Nov 5

Zhang Y, Lin J, Zhao L

41. A systematic comparison of normalization methods for eQTL analysis.

Brief Bioinform

2021 Nov 5

Yang J, Wang D, Yang Y

42. Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing.

Brief Bioinform

2021 Nov 5

Jin X, Zhou W, Luo M

43. Reconciling multiple connectivity scores for drug repurposing.

Brief Bioinform

2021 Nov 5

Samart K, Tuyishime P, Krishnan A

44. nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.

Brief Bioinform

2021 Nov 5

Chen YZ, Wang ZZ, Wang Y

Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identification of Kcr sites. However, experimental approaches for identifying Kcr sites are often time-consuming and expensive when compared with computational approaches. To date, several predictors for Kcr site prediction have been developed, most of which are capable of predicting crotonylation sites on either histones alone or mixed histone and nonhistone proteins together. These methods exhibit high diversity in their algorithms, encoding schemes, feature selection techniques and performance assessment strategies. However, none of them were designed for predicting Kcr sites on nonhistone proteins. Therefore, it is desirable to develop an effective predictor for identifying Kcr sites from the large amount of nonhistone sequence data. For this purpose, we first provide a comprehensive review on six methods for predicting crotonylation sites. Second, we develop a novel deep learning-based computational framework termed as CNNrgb for Kcr site prediction on nonhistone proteins by integrating different types of features. We benchmark its performance against multiple commonly used machine learning classifiers (including random forest, logitboost, naïve Bayes and logistic regression) by performing both 10-fold cross-validation and independent test. The results show that the proposed CNNrgb framework achieves the best performance with high computational efficiency on large datasets. Moreover, to facilitate users' efforts to investigate Kcr sites on human nonhistone proteins, we implement an online server called nhKcr and compare it with other existing tools to illustrate the utility and robustness of our method. The nhKcr web server and all the datasets utilized in this study are freely accessible at http://nhKcr.erc.monash.edu/.

45. Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease.

Brief Bioinform

2021 Nov 5

Xie L, He B, Varathan P

46. Exploring effectiveness of ab-initio protein-protein docking methods on a novel antibacterial protein complex dataset.

Brief Bioinform

2021 Nov 5

Zhang W, Meng Q, Tang J

47. EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps.

Brief Bioinform

2021 Nov 5

He J, Huang SY.

48. NSL2CD: identifying potential circRNA-disease associations based on network embedding and subspace learning.

Brief Bioinform

2021 Nov 5

Xiao Q, Fu Y, Yang Y

49. Comparison of sparse biclustering algorithms for gene expression datasets.

Brief Bioinform

2021 Nov 5

Nicholls K, Wallace C.

MOTIVATION: Gene clustering and sample clustering are commonly used to find patterns in gene expression datasets. However, genes may cluster differently in heterogeneous samples (e.g. different tissues or disease states), whilst traditional methods assume that clusters are consistent across samples. Biclustering algorithms aim to solve this issue by performing sample clustering and gene clustering simultaneously. Existing reviews of biclustering algorithms have yet to include a number of more recent algorithms and have based comparisons on simplistic simulated datasets without specific evaluation of biclusters in real datasets, using less robust metrics. RESULTS: We compared four classes of sparse biclustering algorithms on a range of simulated and real datasets. All algorithms generally struggled on simulated datasets with a large number of genes or implanted biclusters. We found that Bayesian algorithms with strict sparsity constraints had high accuracy on the simulated datasets and did not require any post-processing, but were considerably slower than other algorithm classes. We found that non-negative matrix factorisation algorithms performed poorly, but could be re-purposed for biclustering through a sparsity-inducing post-processing procedure we introduce; one such algorithm was one of the most highly ranked on real datasets. In a multi-tissue knockout mouse RNA-seq dataset, the algorithms rarely returned clusters containing samples from multiple different tissues, whilst such clusters were identified in a human dataset of more closely related cell types (sorted blood cell subsets). This highlights the need for further thought in the design and analysis of multi-tissue studies to avoid differences between tissues dominating the analysis. AVAILABILITY: Code to run the analysis is available at https://github.com/nichollskc/biclust_comp, including wrappers for each algorithm, implementations of evaluation metrics, and code to simulate datasets and perform pre- and post-processing. The full tables of results are available at https://doi.org/10.5281/zenodo.4581206.

50. An effective self-supervised framework for learning expressive molecular global representations to drug discovery.

Brief Bioinform

2021 Nov 5

Li P, Wang J, Qiao Y

51. Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors.

Brief Bioinform

2021 Nov 5

Zhao Y, Wang XG, Ma ZY

52. Forman persistent Ricci curvature (FPRC)-based machine learning models for protein-ligand binding affinity prediction.

Brief Bioinform

2021 Nov 5

Wee J, Xia K.

53. MMFGRN: a multi-source multi-model fusion method for gene regulatory network reconstruction.

Brief Bioinform

2021 Nov 5

He W, Tang J, Zou Q

Lots of biological processes are controlled by gene regulatory networks (GRNs), such as growth and differentiation of cells, occurrence and development of the diseases. Therefore, it is important to persistently concentrate on the research of GRN. The determination of the gene-gene relationships from gene expression data is a complex issue. Since it is difficult to efficiently obtain the regularity behind the gene-gene relationship by only relying on biochemical experimental methods, thus various computational methods have been used to construct GRNs, and some achievements have been made. In this paper, we propose a novel method MMFGRN (for "Multi-source Multi-model Fusion for Gene Regulatory Network reconstruction") to reconstruct the GRN. In order to make full use of the limited datasets and explore the potential regulatory relationships contained in different data types, we construct the MMFGRN model from three perspectives: single time series data model, single steady-data model and time series and steady-data joint model. And, we utilize the weighted fusion strategy to get the final global regulatory link ranking. Finally, MMFGRN model yields the best performance on the DREAM4 InSilico_Size10 data, outperforming other popular inference algorithms, with an overall area under receiver operating characteristic score of 0.909 and area under precision-recall (AUPR) curves score of 0.770 on the 10-gene network. Additionally, as the network scale increases, our method also has certain advantages with an overall AUPR score of 0.335 on the DREAM4 InSilico_Size100 data. These results demonstrate the good robustness of MMFGRN on different scales of networks. At the same time, the integration strategy proposed in this paper provides a new idea for the reconstruction of the biological network model without prior knowledge, which can help researchers to decipher the elusive mechanism of life.

54. REBET: a method to determine the number of cell clusters based on batch effect removal.

Brief Bioinform

2021 Nov 5

Fang ZY, Lin CX, Xu YP

55. Resolving complex structures at oncovirus integration loci with conjugate graph.

Brief Bioinform

2021 Nov 5

Jia W, Xu C, Li SC.

56. Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source.

Brief Bioinform

2021 Nov 5

Noguchi Y, Tachi T, Teramachi H.

57. Improved protein contact prediction using dimensional hybrid residual networks and singularity enhanced loss function.

Brief Bioinform

2021 Nov 5

Si Y, Yan C.

58. Mol2Context-vec: learning molecular representation from context awareness for drug discovery.

Brief Bioinform

2021 Nov 5

Lv Q, Chen G, Zhao L

59. Comparison of approaches to transcriptomic analysis in multi-sampled tumors.

Brief Bioinform

2021 Nov 5

Ku AT, Wilkinson S, Sowalsky AG.

60. Reviewing and assessing existing meta-analysis models and tools.

Brief Bioinform

2021 Nov 5

Makinde FL, Tchamga MSS, Jafali J

61. DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.

Brief Bioinform

2021 Nov 5

Xu Z, Luo M, Lin W

62. De novo generation of dual-target ligands using adversarial training and reinforcement learning.

Brief Bioinform

2021 Nov 5

Lu F, Li M, Min X

63. Mendelian randomization under the omnigenic architecture.

Brief Bioinform

2021 Nov 5

Wang L, Gao B, Fan Y

64. Drug repositioning based on the heterogeneous information fusion graph convolutional network.

Brief Bioinform

2021 Nov 5

Cai L, Lu C, Xu J

65. A heterogeneous network embedding framework for predicting similarity-based drug-target interactions.

Brief Bioinform

2021 Nov 5

An Q, Yu L.

66. Machine learning approaches for drug combination therapies.

Brief Bioinform

2021 Nov 5

Güvenç Paltun B, Kaski S, Mamitsuka H.

67. Distance-guided protein folding based on generalized descent direction.

Brief Bioinform

2021 Nov 5

Wang L, Liu J, Xia Y

68. A comparative benchmark of classic DNA motif discovery tools on synthetic data.

Brief Bioinform

2021 Nov 5

Castellana S, Biagini T, Parca L

69. Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples.

Brief Bioinform

2021 Nov 5

Nadel BB, Oliva M, Shou BL

70. Tracing the evolution of aneuploid cancers by multiregional sequencing with CRUST.

Brief Bioinform

2021 Nov 5

Chattopadhyay S, Karlsson J, Valind A

71. iDeepSubMito: identification of protein submitochondrial localization with deep learning.

Brief Bioinform

2021 Nov 5

Hou Z, Yang Y, Li H

Mitochondria are membrane-bound organelles containing over 1000 different proteins involved in mitochondrial function, gene expression and metabolic processes. Accurate localization of those proteins in the mitochondrial compartments is critical to their operation. A few computational methods have been developed for predicting submitochondrial localization from the protein sequences. Unfortunately, most of these computational methods focus on employing biological features or evolutionary information to extract sequence features, which greatly limits the performance of subsequent identification. Moreover, the efficiency of most computational models is still under explored, especially the deep learning feature, which is promising but requires improvement. To address these limitations, we propose a novel computational method called iDeepSubMito to predict the location of mitochondrial proteins to the submitochondrial compartments. First, we adopted a coding scheme using the ProteinELMo to model the probability distribution over the protein sequences and then represent the protein sequences as continuous vectors. Then, we proposed and implemented convolutional neural network architecture based on the bidirectional LSTM with self-attention mechanism, to effectively explore the contextual information and protein sequence semantic features. To demonstrate the effectiveness of our proposed iDeepSubMito, we performed cross-validation on two datasets containing 424 proteins and 570 proteins respectively, and consisting of four different mitochondrial compartments (matrix, inner membrane, outer membrane and intermembrane regions). Experimental results revealed that our method outperformed other computational methods. In addition, we tested iDeepSubMito on the M187, M983 and MitoCarta3.0 to further verify the efficiency of our method. Finally, the motif analysis and the interpretability analysis were conducted to reveal novel insights into subcellular biological functions of mitochondrial proteins. iDeepSubMito source code is available on GitHub at https://github.com/houzl3416/iDeepSubMito.

72. Improving distance measures between genomic tracks with mutual proximity.

Brief Bioinform

2021 Nov 5

Haschka T, Morlot JB, Carron L

73. ADEIP: an integrated platform of age-dependent expression and immune profiles across human tissues.

Brief Bioinform

2021 Nov 5

Liu X, Chen W, Fang Y

74. A comparison of metabolic labeling and statistical methods to infer genome-wide dynamics of RNA turnover.

Brief Bioinform

2021 Nov 5

Boileau E, Altmüller J, Naarmann-de Vries IS

75. Defining the functional divergence of orthologous genes between human and mouse in the context of miRNA regulation.

Brief Bioinform

2021 Nov 5

Cui C, Zhou Y, Cui Q.

76. Evotuning protocols for Transformer-based variant effect prediction on multi-domain proteins.

Brief Bioinform

2021 Nov 5

Yamaguchi H, Saito Y.

77. Exosomal ncRNAs profiling of mycobacterial infection identified miRNA-185-5p as a novel biomarker for tuberculosis.

Brief Bioinform

2021 Nov 5

Kaushik AC, Wu Q, Lin L

78. COSMIC Cancer Gene Census 3D database: understanding the impacts of mutations on cancer targets.

Brief Bioinform

2021 Nov 5

Alsulami AF, Torres PHM, Moghul I

79. DeepR2cov: deep representation learning on heterogeneous drug networks to discover anti-inflammatory agents for COVID-19.

Brief Bioinform

2021 Nov 5

Wang X, Xin B, Tan W

80. Quercetin for COVID-19 and DENGUE co-infection: a potential therapeutic strategy of targeting critical host signal pathways triggered by SARS-CoV-2 and DENV.

Brief Bioinform

2021 Nov 5

Zheng W, Wu H, Wang T

81. A multi-modal data harmonisation approach for discovery of COVID-19 drug targets.

Brief Bioinform

2021 Nov 5

Chen T, Philip M, Lê Cao KA

82. Methods of privacy-preserving genomic sequencing data alignments.

Brief Bioinform

2021 Nov 5

Lu D, Zhang Y, Zhang L

83. A comparative analysis of RNA-binding proteins binding models learned from RNAcompete, RNA Bind-n-Seq and eCLIP data.

Brief Bioinform

2021 Nov 5

Tripto E, Orenstein Y.

84. Benchmarking association analyses of continuous exposures with RNA-seq in observational studies.

Brief Bioinform

2021 Nov 5

Sofer T, Kurniansyah N, Aguet F

85. An artificial neural network model based on DNA damage response genes to predict outcomes of lower-grade glioma patients.

Brief Bioinform

2021 Nov 5

Chen J, Qian X, He Y

86. Strand Orientation Bias Detector to determine the probability of FFPE sequencing artifacts.

Brief Bioinform

2021 Nov 5

Diossy M, Sztupinszki Z, Krzystanek M

87. Multi-omics data integration and network-based analysis drives a multiplex drug repurposing approach to a shortlist of candidate drugs against COVID-19.

Brief Bioinform

2021 Nov 5

Tomazou M, Bourdakou MM, Minadakis G

88. Bioinformatics and system biology approaches to identify the diseasome and comorbidities complexities of SARS-CoV-2 infection with the digestive tract disorders.

Brief Bioinform

2021 Nov 5

Nashiry MA, Sumi SS, Sharif Shohan MU

Coronavirus Disease 2019 (COVID-19), although most commonly demonstrates respiratory symptoms, but there is a growing set of evidence reporting its correlation with the digestive tract and faeces. Interestingly, recent studies have shown the association of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with gastrointestinal symptoms in infected patients but any sign of respiratory issues. Moreover, some studies have also shown that the presence of live SARS-CoV-2 virus in the faeces of patients with COVID-19. Therefore, the pathophysiology of digestive symptoms associated with COVID-19 has raised a critical need for comprehensive investigative efforts. To address this issue we have developed a bioinformatics pipeline involving a system biological framework to identify the effects of SARS-CoV-2 messenger RNA expression on deciphering its association with digestive symptoms in COVID-19 positive patients. Using two RNA-seq datasets derived from COVID-19 positive patients with celiac (CEL), Crohn's (CRO) and ulcerative colitis (ULC) as digestive disorders, we have found a significant overlap between the sets of differentially expressed genes from SARS-CoV-2 exposed tissue and digestive tract disordered tissues, reporting 7, 22 and 13 such overlapping genes, respectively. Moreover, gene set enrichment analysis, comprehensive analyses of protein-protein interaction network, gene regulatory network, protein-chemical agent interaction network revealed some critical association between SARS-CoV-2 infection and the presence of digestive disorders. The infectome, diseasome and comorbidity analyses also discover the influences of the identified signature genes in other risk factors of SARS-CoV-2 infection to human health. We hope the findings from this pathogenetic analysis may reveal important insights in deciphering the complex interplay between COVID-19 and digestive disorders and underpins its significance in therapeutic development strategy to combat against COVID-19 pandemic.

89. MHCBI: a pipeline for calculating peptide-MHC binding energy using semi-empirical quantum mechanical methods with explicit/implicit solvent models.

Brief Bioinform

2021 Nov 5

Ortiz-Mahecha CA, Agudelo WA, Patarroyo MA

90. Identification of multiple RNAs using feature fusion.

Brief Bioinform

2021 Nov 5

Singh D, Madhawan A, Roy J.

91. KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiency.

Brief Bioinform

2021 Nov 5

An B, Liang M, Chang T

92. Updates to HCOP: the HGNC comparison of orthology predictions tool.

Brief Bioinform

2021 Nov 5

Yates B, Gray KA, Jones TEM

93. Resilience function uncovers the critical transitions in cancer initiation.

Brief Bioinform

2021 Nov 5

Li Y, Zhang SW.

94. SSI-DDI: substructure-substructure interactions for drug-drug interaction prediction.

Brief Bioinform

2021 Nov 5

Nyamabo AK, Yu H, Shi JY.

95. Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.

Brief Bioinform

2021 Nov 5

Wu CK, Zhang XC, Yang ZJ

96. DNAgenie: accurate prediction of DNA-type-specific binding residues in protein sequences.

Brief Bioinform

2021 Nov 5

Zhang J, Ghadermarzi S, Katuwawala A

97. An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction.

Brief Bioinform

2021 Nov 5

Yang Y, Walker TM, Kouchaki S

98. Detecting methylation quantitative trait loci using a methylation random field method.

Brief Bioinform

2021 Nov 5

Lyu C, Huang M, Liu N

99. Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design.

Brief Bioinform

2021 Nov 5

Lv H, Shi L, Berkenpas JW

100. High-dimensional generalized propensity score with application to omics data.

Brief Bioinform

2021 Nov 5

Gao Q, Zhang Y, Liang J

101. FROGS: a powerful tool to analyse the diversity of fungi with special management of internal transcribed spacers.

Brief Bioinform

2021 Nov 5

Bernard M, Rué O, Mariadassou M

102. RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks.

Brief Bioinform

2021 Nov 5

Shi S, Qian Q, Yu S

103. XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.

Brief Bioinform

2021 Nov 5

Withnell E, Zhang X, Sun K

104. Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data.

Brief Bioinform

2021 Nov 5

Qin J, Hu Y, Yao JC

105. DeepFeature: feature selection in nonimage data using convolutional neural network.

Brief Bioinform

2021 Nov 5

Sharma A, Lysenko A, Boroevich KA

106. Genome-wide hierarchical mixed model association analysis.

Brief Bioinform

2021 Nov 5

Hao Z, Gao J, Song Y

107. Identification of miRNA-disease associations via multiple information integration with Bayesian ranking.

Brief Bioinform

2021 Nov 5

Zhu CC, Wang CC, Zhao Y

108. PScL-HDeep: image-based prediction of protein subcellular location in human tissue using ensemble learning of handcrafted and deep learned features with two-layer feature selection.

Brief Bioinform

2021 Nov 5

Ullah M, Han K, Hadi F

109. GESLM algorithm for detecting causal SNPs in GWAS with multiple phenotypes.

Brief Bioinform

2021 Nov 5

Lyu R, Sun J, Xu D

110. bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in vitro drug combination experiments.

Brief Bioinform

2021 Nov 5

Rønneberg L, Cremaschi A, Hanes R

111. ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides.

Brief Bioinform

2021 Nov 5

Timmons PB, Hewage CM.

112. Deep learning-based real-time detection of novel pathogens during sequencing.

Brief Bioinform

2021 Nov 5

Bartoszewicz JM, Genske U, Renard BY.

113. AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches.

Brief Bioinform

2021 Nov 5

Pang Y, Yao L, Jhong JH

114. In silico model for miRNA-mediated regulatory network in cancer.

Brief Bioinform

2021 Nov 5

Ahmed KT, Sun J, Chen W

115. Chromatin loop anchors predict transcript and exon usage.

Brief Bioinform

2021 Nov 5

Zhang Y, Cai Y, Roca X

116. Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles.

Brief Bioinform

2021 Nov 5

Huang Y, Wang P, Zhou W

117. Improved prediction of protein-protein interaction using a hybrid of functional-link Siamese neural network and gradient boosting machines.

Brief Bioinform

2021 Nov 5

Mahapatra S, Sahu SS.

118. Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network.

Brief Bioinform

2021 Nov 5

Li JY, Jin S, Tu XM

119. A novel graph attention model for predicting frequencies of drug-side effects from multi-view data.

Brief Bioinform

2021 Nov 5

Zhao H, Zheng K, Li Y

120. MIMIC: an optimization method to identify cell type-specific marker panel for cell sorting.

Brief Bioinform

2021 Nov 5

Zou M, Duren Z, Yuan Q

121. A comprehensive comparative assessment of 3D molecular similarity tools in ligand-based virtual screening.

Brief Bioinform

2021 Nov 5

Jiang Z, Xu J, Yan A

122. A new method to accurately identify single nucleotide variants using small FFPE breast samples.

Brief Bioinform

2021 Nov 5

Fortunato A, Mallo D, Rupp SM

123. Shall genomic correlation structure be considered in copy number variants detection?

Brief Bioinform

2021 Nov 5

Qin F, Luo X, Cai G

124. Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects.

Brief Bioinform

2021 Nov 5

Huo L, Jiao Li J, Chen L

125. Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.

Brief Bioinform

2021 Nov 5

Liu L, Feng X, Li H

126. Detecting SARS-CoV-2 and its variant strains with a full genome tiling array.

Brief Bioinform

2021 Nov 5

Jiang L, Guo Y, Yu H

127. DITHER: an algorithm for Defining IntraTumor Heterogeneity based on EntRopy.

Brief Bioinform

2021 Nov 5

Li L, Chen C, Wang X.

128. PCLasso: a protein complex-based, group lasso-Cox model for accurate prognosis and risk protein complex discovery.

Brief Bioinform

2021 Nov 5

Wang W, Liu W.

129. A predictive paradigm for COVID-19 prognosis based on the longitudinal measure of biomarkers.

Brief Bioinform

2021 Nov 5

Chen X, Gao W, Li J

130. Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations.

Brief Bioinform

2021 Nov 5

Iqbal S, Li F, Akutsu T

Understanding how a mutation might affect protein stability is of significant importance to protein engineering and for understanding protein evolution genetic diseases. While a number of computational tools have been developed to predict the effect of missense mutations on protein stability protein stability upon mutations, they are known to exhibit large biases imparted in part by the data used to train and evaluate them. Here, we provide a comprehensive overview of predictive tools, which has provided an evolving insight into the importance and relevance of features that can discern the effects of mutations on protein stability. A diverse selection of these freely available tools was benchmarked using a large mutation-level blind dataset of 1342 experimentally characterised mutations across 130 proteins from ThermoMutDB, a second test dataset encompassing 630 experimentally characterised mutations across 39 proteins from iStable2.0 and a third blind test dataset consisting of 268 mutations in 27 proteins from the newly published ProThermDB. The performance of the methods was further evaluated with respect to the site of mutation, type of mutant residue and by ranging the pH and temperature. Additionally, the classification performance was also evaluated by classifying the mutations as stabilizing (∆∆G ≥ 0) or destabilizing (∆∆G < 0). The results reveal that the performance of the predictors is affected by the site of mutation and the type of mutant residue. Further, the results show very low performance for pH values 6-8 and temperature higher than 65 for all predictors except iStable2.0 on the S630 dataset. To illustrate how stability and structure change upon single point mutation, we considered four stabilizing, two destabilizing and two stabilizing mutations from two proteins, namely the toxin protein and bovine liver cytochrome. Overall, the results on S268, S630 and S1342 datasets show that the performance of the integrated predictors is better than the mechanistic or individual machine learning predictors. We expect that this paper will provide useful guidance for the design and development of next-generation bioinformatic tools for predicting protein stability changes upon mutations.

131. HelPredictor models single-cell transcriptome to predict human embryo lineage allocation.

Brief Bioinform

2021 Nov 5

Liang P, Zheng L, Long C

132. WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.

Brief Bioinform

2021 Nov 5

Wang Y, Jiang Y, Yao B

133. Machine learning builds full-QM precision protein force fields in seconds.

Brief Bioinform

2021 Nov 5

Han Y, Wang Z, Wei Z

134. A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains.

Brief Bioinform

2021 Nov 5

Sunuwar J, Azad RK.

135. ARAMIS: From systematic errors of NGS long reads to accurate assemblies.

Brief Bioinform

2021 Nov 5

Sacristán-Horcajada E, González-de la Fuente S, Peiró-Pastor R

136. Characterization and comparison of gene-centered human interactomes.

Brief Bioinform

2021 Nov 5

Mosca E, Bersanelli M, Matteuzzi T

137. DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity.

Brief Bioinform

2021 Nov 5

Li G, Iyer B, Prasath VBS

138. Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries.

Brief Bioinform

2021 Nov 5

Masoudi-Sobhanzadeh Y, Salemi A, Pourseif MM

139. Network-based identification of key master regulators associated with an immune-silent cancer phenotype.

Brief Bioinform

2021 Nov 5

Mall R, Saad M, Roelands J

A cancer immune phenotype characterized by an active T-helper 1 (Th1)/cytotoxic response is associated with responsiveness to immunotherapy and favorable prognosis across different tumors. However, in some cancers, such an intratumoral immune activation does not confer protection from progression or relapse. Defining mechanisms associated with immune evasion is imperative to refine stratification algorithms, to guide treatment decisions and to identify candidates for immune-targeted therapy. Molecular alterations governing mechanisms for immune exclusion are still largely unknown. The availability of large genomic datasets offers an opportunity to ascertain key determinants of differential intratumoral immune response. We follow a network-based protocol to identify transcription regulators (TRs) associated with poor immunologic antitumor activity. We use a consensus of four different pipelines consisting of two state-of-the-art gene regulatory network inference techniques, regularized gradient boosting machines and ARACNE to determine TR regulons, and three separate enrichment techniques, including fast gene set enrichment analysis, gene set variation analysis and virtual inference of protein activity by enriched regulon analysis to identify the most important TRs affecting immunologic antitumor activity. These TRs, referred to as master regulators (MRs), are unique to immune-silent and immune-active tumors, respectively. We validated the MRs coherently associated with the immune-silent phenotype across cancers in The Cancer Genome Atlas and a series of additional datasets in the Prediction of Clinical Outcomes from Genomic Profiles repository. A downstream analysis of MRs specific to the immune-silent phenotype resulted in the identification of several enriched candidate pathways, including NOTCH1, TGF-$\beta $, Interleukin-1 and TNF-$\alpha $ signaling pathways. TGFB1I1 emerged as one of the main negative immune modulators preventing the favorable effects of a Th1/cytotoxic response.

140. WDNE: an integrative graphical model for inferring differential networks from multi-platform gene expression data with missing values.

Brief Bioinform

2021 Nov 5

Ou-Yang L, Cai D, Zhang XF

141. NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.

Brief Bioinform

2021 Nov 5

Hasan MM, Alam MA, Shoombuatong W

142. Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor.

Brief Bioinform

2021 Nov 5

Nacer DF, Liljedahl H, Karlsson A

143. Prediction of tumor purity from gene expression data using machine learning.

Brief Bioinform

2021 Nov 5

Koo B, Rhee JK.

144. PreTP-EL: prediction of therapeutic peptides based on ensemble learning.

Brief Bioinform

2021 Nov 5

Guo Y, Yan K, Lv H

145. Leveraging the attention mechanism to improve the identification of DNA N6-methyladenine sites.

Brief Bioinform

2021 Nov 5

Zhang Y, Liu Y, Xu J

146. DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data.

Brief Bioinform

2021 Nov 5

Chen J, Cheong C, Lan L

147. Recent omics-based computational methods for COVID-19 drug discovery and repurposing.

Brief Bioinform

2021 Nov 5

Tayara H, Abdelbaky I, To Chong K.

148. Prediction of RBP binding sites on circRNAs using an LSTM-based deep sequence learning architecture.

Brief Bioinform

2021 Nov 5

Wang Z, Lei X.

149. Single-cell Hi-C data analysis: safety in numbers.

Brief Bioinform

2021 Nov 5

Galitsyna AA, Gelfand MS.

150. Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization.

Brief Bioinform

2021 Nov 5

Chen X, Zhou C, Wang CC

151. A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery.

Brief Bioinform

2021 Nov 5

Rintala TJ, Federico A, Latonen L

152. NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods.

Brief Bioinform

2021 Nov 5

Jiang M, Zhao B, Luo S

153. Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.

Brief Bioinform

2021 Nov 5

Sharifi-Noghabi H, Jahangiri-Tazehkand S, Smirnov P

154. Identifying complex gene-gene interactions: a mixed kernel omnibus testing approach.

Brief Bioinform

2021 Nov 5

Liu Y, Gao Y, Fang R

155. Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.

Brief Bioinform

2021 Nov 5

Dogan H, Hakguder Z, Madadjim R

156. A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling.

Brief Bioinform

2021 Nov 5

Piyawajanusorn C, Nguyen LC, Ghislat G

157. Deep fusion learning facilitates anatomical therapeutic chemical recognition in drug repurposing and discovery.

Brief Bioinform

2021 Nov 5

Wang X, Liu M, Zhang Y

158. Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.

Brief Bioinform

2021 Nov 5

Fan K, Cheng L, Li L.

159. Local ancestry inference in heterogeneous populations-Are recent recombination events more relevant?

Brief Bioinform

2021 Nov 5

van Eeden G, Uren C, van der Spuy G

160. Network-guided identification of cancer-selective combinatorial therapies in ovarian cancer.

Brief Bioinform

2021 Nov 5

He L, Bulanova D, Oikkonen J

161. Genetic mechanisms of COVID-19 and its association with smoking and alcohol consumption.

Brief Bioinform

2021 Nov 5

Rao S, Baranova A, Cao H

162. scAdapt: virtual adversarial domain adaptation network for single cell RNA-seq data classification across platforms and species.

Brief Bioinform

2021 Nov 5

Zhou X, Chai H, Zeng Y

163. Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison.

Brief Bioinform

2021 Nov 5

Song B, Li F, Liu Y

164. Recognizing binding sites of poorly characterized RNA-binding proteins on circular RNAs using attention Siamese network.

Brief Bioinform

2021 Nov 5

Wu H, Pan X, Yang Y

165. AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom.

Brief Bioinform

2021 Nov 5

Sharma R, Shrivastava S, Kumar Singh S

166. Tripal, a community update after 10 years of supporting open source, standards-based genetic, genomic and breeding databases.

Brief Bioinform

2021 Nov 5

Staton M, Cannon E, Sanderson LA

167. DISMIR: Deep learning-based noninvasive cancer detection by integrating DNA sequence and methylation information of individual cell-free DNA reads.

Brief Bioinform

2021 Nov 5

Li J, Wei L, Zhang X

168. Performance evaluation of transcriptomics data normalization for survival risk prediction.

Brief Bioinform

2021 Nov 5

Ni A, Qin LX.

169. A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma.

Brief Bioinform

2021 Nov 5

Gui CP, Wei JH, Chen YH

Increasing evidences show the clinical significance of the interaction between hypoxia and immune in clear cell renal cell carcinoma (ccRCC) microenvironment. However, reliable prognostic signatures based on a combination of hypoxia and immune have not been well established. Moreover, many studies have only used RNA-seq profiles to screen the prognosis feature of ccRCC. Presently, there is no comprehensive analysis of multiomics data to mine a better one. Thus, we try and get it. First, t-SNE and ssGSEA analysis were used to establish tumor subtypes related to hypoxia-immune, and we investigated the hypoxia-immune-related differences in three types of genetic or epigenetic characteristics (gene expression profiles, somatic mutation, and DNA methylation) by analyzing the multiomics data from The Cancer Genome Atlas (TCGA) portal. Additionally, a four-step strategy based on lasso regression and Cox regression was used to construct a satisfying prognostic model, with average 1-year, 3-year and 5-year areas under the curve (AUCs) equal to 0.806, 0.776 and 0.837. Comparing it with other nine known prognostic biomarkers and clinical prognostic scoring algorithms, the multiomics-based signature performs better. Then, we verified the gene expression differences in two external databases (ICGC and SYSU cohorts). Next, eight hub genes were singled out and seven hub genes were validated as prognostic genes in SYSU cohort. Furthermore, it was indicated high-risk patients have a better response for immunotherapy in immunophenoscore (IPS) analysis and TIDE algorithm. Meanwhile, estimated by GDSC and cMAP database, the high-risk patients showed sensitive responses to six chemotherapy drugs and six candidate small-molecule drugs. In summary, the signature can accurately predict the prognosis of ccRCC and may shed light on the development of novel hypoxia-immune biomarkers and target therapy of ccRCC.

170. Improving protein fold recognition using triplet network and ensemble deep learning.

Brief Bioinform

2021 Nov 5

Liu Y, Han K, Zhu YH

Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer's representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue-residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.

171. Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.

Brief Bioinform

2021 Nov 5

Basith S, Hasan MM, Lee G

Enhancers are deoxyribonucleic acid (DNA) fragments which when bound by transcription factors enhance the transcription of related genes. Due to its sporadic distribution and similar fractions, identification of enhancers from the human genome seems a daunting task. Compared to the traditional experimental approaches, computational methods with easy-to-use platforms could be efficiently applied to annotate enhancers' functions and physiological roles. In this aspect, several bioinformatics tools have been developed to identify enhancers. Despite their spectacular performances, existing methods have certain drawbacks and limitations, including fixed length of sequences being utilized for model development and cell-specificity negligence. A novel predictor would be beneficial in the context of genome-wide enhancer prediction by addressing the above-mentioned issues. In this study, we constructed new datasets for eight different cell types. Utilizing these data, we proposed an integrative machine learning (ML)-based framework called Enhancer-IF for identifying cell-specific enhancers. Enhancer-IF comprehensively explores a wide range of heterogeneous features with five commonly used ML methods (random forest, extremely randomized tree, multilayer perceptron, support vector machine and extreme gradient boosting). Specifically, these five classifiers were trained with seven encodings and obtained 35 baseline models. The output of these baseline models was integrated and again inputted to five classifiers for the construction of five meta-models. Finally, the integration of five meta-models through ensemble learning improved the model robustness. Our proposed approach showed an excellent prediction performance compared to the baseline models on both training and independent datasets in different cell types, thus highlighting the superiority of our approach in the identification of the enhancers. We assume that Enhancer-IF will be a valuable tool for screening and identifying potential enhancers from the human DNA sequences.

172. Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design.

Brief Bioinform

2021 Nov 5

Rezaei S, Sefidbakht Y, Uskoković V.

173. LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec.

Brief Bioinform

2021 Nov 5

Tsukiyama S, Hasan MM, Fujii S

174. Consensus clustering of single-cell RNA-seq data by enhancing network affinity.

Brief Bioinform

2021 Nov 5

Cui Y, Zhang S, Liang Y

175. An integrated approach for copy number variation discovery in parent-offspring trios.

Brief Bioinform

2021 Nov 5

Liu Y, Wu X, Wang Y.

176. Understanding structural malleability of the SARS-CoV-2 proteins and relation to the comorbidities.

Brief Bioinform

2021 Nov 5

Sen S, Dey A, Bandhyopadhyay S

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent of the coronavirus disease (COVID-19), is a part of the $\beta $-Coronaviridae family. The virus contains five major protein classes viz., four structural proteins [nucleocapsid (N), membrane (M), envelop (E) and spike glycoprotein (S)] and replicase polyproteins (R), synthesized as two polyproteins (ORF1a and ORF1ab). Due to the severity of the pandemic, most of the SARS-CoV-2-related research are focused on finding therapeutic solutions. However, studies on the sequences and structure space throughout the evolutionary time frame of viral proteins are limited. Besides, the structural malleability of viral proteins can be directly or indirectly associated with the dysfunctionality of the host cell proteins. This dysfunctionality may lead to comorbidities during the infection and may continue at the post-infection stage. In this regard, we conduct the evolutionary sequence-structure analysis of the viral proteins to evaluate their malleability. Subsequently, intrinsic disorder propensities of these viral proteins have been studied to confirm that the short intrinsically disordered regions play an important role in enhancing the likelihood of the host proteins interacting with the viral proteins. These interactions may result in molecular dysfunctionality, finally leading to different diseases. Based on the host cell proteins, the diseases are divided in two distinct classes: (i) proteins, directly associated with the set of diseases while showing similar activities, and (ii) cytokine storm-mediated pro-inflammation (e.g. acute respiratory distress syndrome, malignancies) and neuroinflammation (e.g. neurodegenerative and neuropsychiatric diseases). Finally, the study unveils that males and postmenopausal females can be more vulnerable to SARS-CoV-2 infection due to the androgen-mediated protein transmembrane serine protease 2.

177. Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.

Brief Bioinform

2021 Nov 5

Jiang L, Yu H, Li J

178. Co-mutation modules capture the evolution and transmission patterns of SARS-CoV-2.

Brief Bioinform

2021 Nov 5

Qin L, Ding X, Li Y

179. PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture.

Brief Bioinform

2021 Nov 5

Cao X, He W, Chen Z

180. Likelihood-based tests for detecting circadian rhythmicity and differential circadian patterns in transcriptomic applications.

Brief Bioinform

2021 Nov 5

Ding H, Meng L, Liu AC

181. The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis.

Brief Bioinform

2021 Nov 5

Tang F, Zhang L, Xu L

182. Web repositories of natural agents promote pests and pathogenic microbes management.

Brief Bioinform

2021 Nov 5

Jin Y, Wang Z, Dong AY

183. DevOmics: an integrated multi-omics database of human and mouse early embryo.

Brief Bioinform

2021 Nov 5

Yan Z, An J, Peng Y

184. iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types.

Brief Bioinform

2021 Nov 5

Xiao X, Shao YT, Cheng X

185. Large scale RNA-binding proteins/LncRNAs interaction analysis to uncover lncRNA nuclear localization mechanisms.

Brief Bioinform

2021 Nov 5

Huang Y, Qiao Y, Zhao Y

186. A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine.

Brief Bioinform

2021 Nov 5

Dotolo S, Marabotti A, Rachiglio AM

MOTIVATION: Assessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on 'multiple network analysis' in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases. RESULTS: By using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered. AVAILABILITY: The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55]. SUPPLEMENTARY INFORMATION: A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.

187. COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model.

Brief Bioinform

2021 Nov 5

Gogate N, Lyman D, Bell A

188. ProtCHOIR: a tool for proteome-scale generation of homo-oligomers.

Brief Bioinform

2021 Nov 5

Torres PHM, Rossi AD, Blundell TL.

The rapid developments in gene sequencing technologies achieved in the recent decades, along with the expansion of knowledge on the three-dimensional structures of proteins, have enabled the construction of proteome-scale databases of protein models such as the Genome3D and ModBase. Nevertheless, although gene products are usually expressed as individual polypeptide chains, most biological processes are associated with either transient or stable oligomerisation. In the PDB databank, for example, ~40% of the deposited structures contain at least one homo-oligomeric interface. Unfortunately, databases of protein models are generally devoid of multimeric structures. To tackle this particular issue, we have developed ProtCHOIR, a tool that is able to generate homo-oligomeric structures in an automated fashion, providing detailed information for the input protein and output complex. ProtCHOIR requires input of either a sequence or a protomeric structure that is queried against a pre-constructed local database of homo-oligomeric structures, then extensively analyzed using well-established tools such as PSI-Blast, MAFFT, PISA and Molprobity. Finally, MODELLER is employed to achieve the construction of the homo-oligomers. The output complex is thoroughly analyzed taking into account its stereochemical quality, interfacial stabilities, hydrophobicity and conservation profile. All these data are then summarized in a user-friendly HTML report that can be saved or printed as a PDF file. The software is easily parallelizable and also outputs a comma-separated file with summary statistics that can straightforwardly be concatenated as a spreadsheet-like document for large-scale data analyses. As a proof-of-concept, we built oligomeric models for the Mabellini Mycobacterium abscessus structural proteome database. ProtCHOIR can be run as a web-service and the code can be obtained free-of-charge at http://lmdm.biof.ufrj.br/protchoir.

189. Utilizing graph machine learning within drug discovery and development.

Brief Bioinform

2021 Nov 5

Gaudelet T, Day B, Jamasb AR

190. In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor.

Brief Bioinform

2021 Nov 5

Zhang Y, He X, Zhai J

191. Chrom-Lasso: a lasso regression-based model to detect functional interactions using Hi-C data.

Brief Bioinform

2021 Nov 5

Lu J, Wang X, Sun K

192. Identification of genetic variations associated with drug resistance in non-small cell lung cancer patients undergoing systemic treatment.

Brief Bioinform

2021 Nov 5

Luo R, Ge C, Xiao X

193. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph.

Brief Bioinform

2021 Nov 5

Chu Y, Wang X, Dai Q

194. ConsRM: collection and large-scale prediction of the evolutionarily conserved RNA methylation sites, with implications for the functional epitranscriptome.

Brief Bioinform

2021 Nov 5

Song B, Chen K, Tang Y

195. Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.

Brief Bioinform

2021 Nov 5

Chen K, Xu H, Lei Y

196. Knowledge bases and software support for variant interpretation in precision oncology.

Brief Bioinform

2021 Nov 5

Borchert F, Mock A, Tomczak A

197. ProTICS reveals prognostic impact of tumor infiltrating immune cells in different molecular subtypes.

Brief Bioinform

2021 Nov 5

Liu S, Zhang Y, Shang X

Different subtypes of the same cancer often show distinct genomic signatures and require targeted treatments. The differences at the cellular and molecular levels of tumor microenvironment in different cancer subtypes have significant effects on tumor pathogenesis and prognostic outcomes. Although there have been significant researches on the prognostic association of tumor infiltrating lymphocytes in selected histological subtypes, few investigations have systemically reported the prognostic impacts of immune cells in molecular subtypes, as quantified by machine learning approaches on multi-omics datasets. This paper describes a new computational framework, ProTICS, to quantify the differences in the proportion of immune cells in tumor microenvironment and estimate their prognostic effects in different subtypes. First, we stratified patients into molecular subtypes based on gene expression and methylation profiles by applying nonnegative tensor factorization technique. Then we quantified the proportion of cell types in each specimen using an mRNA-based deconvolution method. For tumors in each subtype, we estimated the prognostic effects of immune cell types by applying Cox proportional hazard regression. At the molecular level, we also predicted the prognosis of signature genes for each subtype. Finally, we benchmarked the performance of ProTICS on three TCGA datasets and another independent METABRIC dataset. ProTICS successfully stratified tumors into different molecular subtypes manifested by distinct overall survival. Furthermore, the different immune cell types showed distinct prognostic patterns with respect to molecular subtypes. This study provides new insights into the prognostic association between immune cells and molecular subtypes, showing the utility of immune cells as potential prognostic markers. Availability: R code is available at https://github.com/liu-shuhui/ProTICS.

198. StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides.

Brief Bioinform

2021 Nov 5

Charoenkwan P, Chiangjong W, Nantasenamat C

The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well as by immune cells for activating aggressive inflammation. IL-6 inducing peptides are derived from pathogens and can be used as diagnostic biomarkers for predicting various stages of disease severity as well as being used as IL-6 inhibitors for the suppression of aggressive multi-signaling immune responses. Thus, the accurate identification of IL-6 inducing peptides is of great importance for investigating their mechanism of action as well as for developing diagnostic and immunotherapeutic applications. This study proposes a novel stacking ensemble model (termed StackIL6) for accurately identifying IL-6 inducing peptides. More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest). To enhance the utility of baseline models, they were effectively and systematically integrated through a stacking strategy to build the final meta-based model. Extensive benchmarking experiments demonstrated that StackIL6 could achieve significantly better performance than the existing method (IL6PRED) and outperformed its constituent baseline models on both training and independent test datasets, which thereby support its excellent discrimination and generalization abilities. To facilitate easy access to the StackIL6 model, it was established as a freely available web server accessible at http://camt.pythonanywhere.com/StackIL6. It is anticipated that StackIL6 can help to facilitate rapid screening of promising IL-6 inducing peptides for the development of diagnostic and immunotherapeutic applications in the future.

199. Multi-view Multichannel Attention Graph Convolutional Network for miRNA-disease association prediction.

Brief Bioinform

2021 Nov 5

Tang X, Luo J, Shen C

200. Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision.

Brief Bioinform

2021 Nov 5

Davies P, Jones M, Liu J

201. A novel computational framework for genome-scale alternative transcription units prediction.

Brief Bioinform

2021 Nov 5

Wang Q, Liu Z, Yan B