The CRISPR‒Cas system acts as a bacterial defense mechanism by conferring adaptive immunity and limiting genetic reshuffling. However, under adverse environmental hazards, bacteria can employ their CRISPR‒Cas system to exchange genes that are vital for adaptation and survival. Levilactobacillus brevis is a lactic acid bacterium with great potential for commercial purposes because it can be genetically manipulated to enhance its functionality and nutritional value. Nevertheless, the CRISPR‒Cas system might interfere with the genetic modification process. Additionally, little is known about the CRISPR‒Cas system in this industrially important microorganism. Here, we investigate the prevalence, diversity, and targets of CRISPR‒Cas systems in the genus Levilactobacillus, further focusing on complete genomes of L. brevis. Using the CRISPRCasFinder webserver, we identified 801 putative CRISPR-Cas systems in the genus Levilactobacillus. Further investigation focusing on the complete genomes of L. brevis revealed 54 putative CRISPR-Cas systems. Of these, 46 were orphan CRISPRs, and eight were CRISPR‒Cas systems. The type II-A CRISPR‒Cas system is the most common in Levilactobacillus and L. brevis complete genomes. Analysis of the spacer's target showed that the CRISPR‒Cas systems of L. brevis mainly target the enterococcal plasmids. Comparative analysis of putative CRISPR-Cas loci in Levilactobacillus brevis.

G-quadruplex (G4), a non-canonical nucleic acid structure, has been suggested to play a key role in important cellular processes including transcription, replication and cancer development. Recently, high-throughput sequencing approaches for G4 detection have provided a large amount of experimentally identified G4 data that reveal genome-wide G4 landscapes and enable the development of new methods for predicting potential G4s from sequences. Although several existing databases provide G4 experimental data and relevant biological information from different perspectives, there is no dedicated database to collect and analyze DNA G4 experimental data genome-widely. Here, we constructed G4Bank, a database of experimentally identified DNA G-quadruplex sequences. A total of 6,915,983 DNA G4s were collected from 13 organisms, and state-of-the-art prediction methods were performed to filter and analyze the G4 data. Therefore, G4Bank will facilitate users to access comprehensive G4 experimental data and enable sequence feature analysis of G4 for further investigation. The database of the experimentally identified DNA G-quadruplex sequences can be accessed at http://tubic.tju.edu.cn/g4bank/ .

RNA folding prediction is very meaningful and challenging. The molecular dynamics simulation (MDS) of all atoms (AA) is limited to the folding of small RNA molecules. At present, most of the practical models are coarse grained (CG) model, and the coarse-grained force field (CGFF) parameters usually depend on known RNA structures. However, the limitation of the CGFF is obvious that it is difficult to study the modified RNA. Based on the 3 beads model (AIMS_RNA_B3), we proposed the AIMS_RNA_B5 model with three beads representing a base and two beads representing the main chain (sugar group and phosphate group). We first run the all atom molecular dynamic simulation (AAMDS), and fit the CGFF parameter with the AA trajectory. Then perform the coarse-grained molecular dynamic simulation (CGMDS). AAMDS is the foundation of CGMDS. CGMDS is mainly to carry out the conformation sampling based on the current AAMDS state and improve the folding speed. We simulated the folding of three RNAs, which belong to hairpin, pseudoknot and tRNA respectively. Compared to the AIMS_RNA_B3 model, the AIMS_RNA_B5 model is more reasonable and performs better.

Gene expression as an unstable form of cell characterization has been widely used for single-cell analyses. Although there are cell-specific networks (CSN) to explore stable gene associations within a single cell, the amount of information in CSN is huge and there is no method to measure the interaction level between genes. Therefore, this paper presents a two-level approach to reconstructing single-cell features, which transforms the original gene expression feature into the gene ontology feature and gene interaction feature. Specifically, we first squeeze all CSNs into a cell network feature matrix (CNFM) by fusing the global position and neighborhood influence of genes. Next, we propose a computational method of gene gravitation based on CNFM to quantify the extent of gene-gene interaction, and we can construct a gene gravitation network for single cells. Finally, we further design a novel index of gene gravitation entropy to quantitatively evaluate the level of single-cell differentiation. The experiments on eight different scRNA-seq datasets show the effectiveness and broad application prospects of our method.

Chemogenomics, also known as proteochemometrics, covers various computational methods for predicting interactions between related drugs and targets on large-scale data. Chemogenomics is used in the early stages of drug discovery to predict the off-target effects of proteins against therapeutic candidates. This study aims to predict unknown ligand-target interactions using one-dimensional SMILES as inputs for ligands and binding site residues for proteins in a computationally efficient manner. We first formulate a Deep learning CNN model using one-dimensional SMILES for drugs and motif-rich binding pocket subsequences of proteins as inputs. We evaluate and compare the proposed deep learning model trained on expert-based features against shallow feature-based machine learning methods. The proposed method achieved better or similar performance on the MSE and AUPR metrics than the shallow methods. Additionally, We show that our deep learning model, DeepPS is computationally more efficient than the deep learning model trained on full-length raw sequences of proteins. We conclude that a beneficial research approach would be to integrate structural information of proteins for modeling drug-target interaction prediction of large datasets for more interpretability, high throughput, and broad applicability.

Numerous scientific evidences have revealed that long non-coding RNAs (lncRNAs) are involved in the progression of human complex diseases and biological life activities. Therefore, identifying novel and potential disease-related lncRNAs is helpful to diagnosis, prognosis and therapy of many human complex diseases. Since traditional laboratory experiments are cost and time-consuming, a great quantity of computer algorithms have been proposed for predicting the relationships between lncRNAs and diseases. However, there are still much room for the improvement. In this paper, we introduce an accurate framework named LDAEXC to infer LncRNA-Disease Associations with deep autoencoder and XGBoost Classifier. LDAEXC utilizes different similarity views of lncRNAs and human diseases to construct features for each data sources. Then, the reduced features are obtained by feeding the constructed feature vectors into a deep autoencoder, and at last an XGBoost classifier is leveraged to calculate the latent lncRNA-disease-associated scores using reduced features. The fivefold cross-validation experiments on four datasets showed that LDAEXC reached AUC scores of 0.9676 ± 0.0043, 0.9449 ± 0.022, 0.9375 ± 0.0331 and 0.9556 ± 0.0134, respectively, significantly higher than other advanced similar computer methods. Extensive experiment results and case studies of two complex diseases (colon and breast cancers) further indicated the practicability and excellent prediction performance of LDAEXC in inferring unknown lncRNA-disease associations. TLDAEXC utilizes disease semantic similarity, lncRNA expression similarity, and Gaussian interaction profile kernel similarity of lncRNAs and diseases for feature construction. The constructed features are fed to a deep autoencoder to extract reduced features, and an XGBoost classifier is used to predict the lncRNA-disease associations based on the reduced features. The fivefold and tenfold cross-validation experiments on a benchmark dataset showed that LDAEXC could achieve AUC scores of 0.9676 and 0.9682, respectively, significantly higher than other state-of-the-art similar methods.

Over the last few years, an increasing number of protein mis-localization events have been reported under various conditions. It is important to understand these events and their relationship with complex disorders. Although many efforts had been made in establishing models with statistical or machine learning algorithms, a comprehensive database resource is still missing. Since the records of experimental-validated protein mis-localization events spread across many literatures, a collection of all these reports in a unique website is demanded. In this paper, we created the dbMisLoc database by manually curating conditional protein mis-localization events from various literatures. The dbMisLoc database records the protein localizations, mis-localizations, conditions for mis-localization, and the original reports. The dbMisLoc database allows the users to intuitively view, search, visualize and download protein mis-localization records. The dbMisLoc database integrates a BLAST search engine, which can search mis-localized proteins that are similar to user queries. The dbMisLoc database can be accessed directly through ( https://dbml.pufengdu.org ). The source code of dbMisLoc database is available from the GitHub repository ( https://github.com/quinlanW/dbMisLoc ) for free. Users can host their own mirrors of dbMisLoc database on their own servers. dbMisLoc is database for manually curated protein mis-localization events. It contains mis-localization events in 14 categories of conditions such as diseases, drug treatments and environmental stresses.

DNA methylation-based precision tumor early diagnostics is emerging as state-of-the-art technology that could capture early cancer signs 3 ~ 5 years in advance, even for clinically homogenous groups. Presently, the sensitivity of early detection for many tumors is ~ 30%, which needs significant improvement. Nevertheless, based on the genome-wide DNA methylation data, one could comprehensively characterize tumors' entire molecular genetic landscape and their subtle differences. Therefore, novel high-performance methods must be modeled by considering unbiased information using excessively available DNA methylation data. To fill this gap, we have designed a computational model involving a self-attention graph convolutional network and multi-class classification support vector machine to identify the 11 most common cancers using DNA methylation data. The self-attention graph convolutional network automatically learns key methylation sites in a data-driven way. Then, multi-tumor early diagnostics is realized by training a multi-class classification support vector machine based on the selected methylation sites. We evaluated our model's performance through several data sets of experiments, and our results demonstrate the effectiveness of the selected key methylation sites, which are highly relevant for blood diagnosis. The pipeline of the self-attention graph convolutional network based computational framework.

DNA computing is a very efficient way to calculate, but it relies on high-quality DNA sequences, but it is difficult to design high-quality DNA sequences. The sequence it is looking for must meet multiple conflicting constraints at the same time to meet the requirements of DNA calculation. Therefore, we propose an improved arithmetic optimization algorithm of billiard algorithm to optimize the DNA sequence. This paper contributes as follows. The introduction to the good point set initialization to obtain high-quality solutions improves the optimization efficiency. The billiard hitting strategy was used to change the position of the population to enhance the global search scope. The use of a stochastic lens opposites learning mechanism can increase the capacity of the algorithm to get rid of locally optimal. The harmonic search algorithm is introduced to clarify some unqualified secondary structures and improve the quality of the solution. 12 benchmark functions and six other algorithms are used for comparison and ablation experiments to ensure the effectiveness of the algorithms. Finally, the DNA sequences we designed are of higher quality compared to other advanced algorithms.

The search for potential drug-disease associations (DDA) can speed up drug development cycles, reduce costly wasted resources, and accelerate disease treatment by repurposing existing drugs that can control further disease progression. As technologies such as deep learning continue to mature, many researchers tend to use emerging technologies to predict potential DDA. The performance of DDA prediction is still challenging and there is some space for improvement due to issues such as the small number of existing associations and possible noise in the data. To better predict DDA, we propose a computational approach based on hypergraph learning with subgraph matching (HGDDA). In particular, HGDDA first extracts feature subgraph information in the validated drug-disease association network and proposes a negative sampling strategy based on similarity network to reduce the data imbalance. Second, the hypergraph Unet module is used by extracting Finally, the potential DDA is predicted by designing a hypergraph combination module to convolution and pooling the two constructed hypergraphs separately, and calculating the difference information between the subgraphs using cosine similarity for node matching. The performance of HGDDA is verified under two standard datasets by 10-fold cross-validation (10-CV), and the results outperform existing drug-disease prediction methods. In addition, to validate the overall utility of the model, the top 10 drugs for the specific disease are predicted through the case study and validated using the CTD database.

With the rapid development of DNA (deoxyribonucleic acid) storage technologies, storing digital images in DNA is feasible. Meanwhile, the information security in DNA storage system is still a problem to solve. Therefore, in this paper, we propose a DNA storage-oriented image encryption algorithm utilizing the information processing mechanisms in molecule biology. The basic idea is to perform pixel replacement by gene hybridization, and implement dual diffusion by pixel diffusion and gene mutation. The ciphertext DNA image can be synthesized and stored in DNA storage system after encryption. Experimental results demonstrate it can resist common attacks, and shows a strong robustness against sequence loss and base substitution errors in the DNA storage channel. A DNA storage-oriented image encryption algorithm based on gene hybridization and gene mutation, First, we scramble rows and columns of the plaintext image by dynamic Josephus traversing. Second, we replace the pixels by gene hybridization. Finally, we diffuse the image matrix in binary domain and encode pixels into 8-base strands which are later further diffused by gene mutation. The ciphertext image can be synthesized according to the mutant gene codes and stored in any DNA storage system.

Nodules of thyroid cancer occur in the cells of the thyroid as benign or malign types. Thyroid sonographic images are mostly used for diagnosis of thyroid cancer. The aim of this study is to introduce a computer-aided diagnosis system that can classify the thyroid nodules with high accuracy using the data gathered from ultrasound images. Acquisition and labeling of sub-images were performed by a specialist physician. Then the number of these sub-images were increased using data augmentation methods. Deep features were obtained from the images using a pre-trained deep neural network. The dimensions of the features were reduced and features were improved. The improved features were combined with morphological and texture features. This feature group was rated by a value called similarity coefficient value which was obtained from a similarity coefficient generator module. The nodules were classified as benign or malignant using a multi-layer deep neural network with a pre-weighting layer designed with a novel approach. In this study, a novel multi-layer computer-aided diagnosis system was proposed for thyroid cancer detection. In the first layer of the system, a novel feature extraction method based on the class similarity of images was developed. In the second layer, a novel pre-weighting layer was proposed by modifying the genetic algorithm. The proposed system showed superior performance in different metrics compared to the literature.

Brain tumors are one of the most dangerous health problems for adults and children in many countries. Any failure in the diagnosis of brain tumors may lead to shortening of human life. Accurate and timely diagnosis of brain tumors provides appropriate treatment to increase the patient's chances of survival. Due to the different characteristics of tumors, one of the challenging problems is the classification of three types of brain tumors. With the advent of deep learning (DL) models, three classes of brain tumor classification have been addressed. However, the accuracy of these methods requires significant improvements in brain image classification. The main goal of this article is to design a new method for classifying the three types of brain tumors with extremely high accuracy. In this paper, we propose a novel deep stacked ensemble model called "BMRI-NET" that can detect brain tumors from MR images with high accuracy and recall. The stacked ensemble proposed in this article adapts three pre-trained models, namely DenseNe201, ResNet152V2, and InceptionResNetV2, to improve the generalization capability. We combine decisions from the three models using the stacking technique to obtain final results that are much more accurate than individual models for detecting brain tumors. The efficacy of the proposed model is evaluated on the Figshare brain MRI dataset of three types of brain tumors consisting of 3064 images. The experimental results clearly highlight the robustness of the proposed BMRI-NET model by achieving an overall classification of 98.69% and an average recall, F1-score and MCC of 98.33%, 98.40, and 97.95%, respectively. The results indicate that the proposed BMRI-NET model is superior to existing methods and can assist healthcare professionals in the diagnosis of brain tumors.

Circular RNAs (circRNAs) participate in the regulation of biological processes by binding to specific proteins and thus influence transcriptional processes. In recent years, circRNAs have become an emerging hotspot in RNA research. Due to powerful learning ability, the various deep learning frameworks have been used to predict the binding sites of RNA-binding protein (RPB) on circRNAs. These methods usually perform only single-level feature extraction of sequence information. However, the feature acquisition may be inadequate for single-level extraction. Generally, the features of deep and shallow layers of neural network can complement each other and are both important for binding site prediction tasks. Based on this concept, we propose a method that combines deep and shallow features, namely CRBP-HFEF. Specifically, features are first extracted and expanded for different levels of network. Then, the expanded deep and shallow features are fused and fed into the classification network, which finally determines whether they are binding sites. Compared to several existing methods, the experimental results on multiple datasets show that the proposed method achieves significant improvements in a number of metrics (with an average AUC of 0.9855). Moreover, much sufficient ablation experiments are also performed to verify the effectiveness of the hierarchical feature expansion strategy.

Chest radiography is a widely used diagnostic imaging procedure in medical practice, which involves prompt reporting of future imaging tests and diagnosis of diseases in the images. In this study, a critical phase in the radiology workflow is automated using the three convolutional neural network (CNN) models, viz. DenseNet121, ResNet50, and EfficientNetB1 for fast and accurate detection of 14 class labels of thoracic pathology diseases based on chest radiography. These models were evaluated on an AUC score for normal versus abnormal chest radiographs using 112120 chest X-ray14 datasets containing various class labels of thoracic pathology diseases to predict the probability of individual diseases and warn clinicians of potential suspicious findings. With DenseNet121, the AUROC scores for hernia and emphysema were predicted as 0.9450 and 0.9120, respectively. Compared to the score values obtained for each class on the dataset, the DenseNet121 outperformed the other two models. This article also aims to develop an automated server to capture fourteen thoracic pathology disease results using a tensor processing unit (TPU). The results of this study demonstrate that our dataset can be used to train models with high diagnostic accuracy for predicting the likelihood of 14 different diseases in abnormal chest radiographs, enabling accurate and efficient discrimination between different types of chest radiographs. This has the potential to bring benefits to various stakeholders and improve patient care.

Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can screen drug combinations in advance and reduce the waste of laboratory resources. In this research, we proposed a model that utilizes graph autoencoder and convolutional neural networks to predict drug synergy (GAECDS). Our methods include a graph convolutional neural network as an encoder to encode drug features and use a matrix factorization method as a decoder. Multilayer perceptron (MLP) was applied to process cell line features and combine them with drug features. Furthermore, the latent vectors generated during the encoding process are being used to predict drug synergistic scores using a convolutional neural network. By measuring prediction performance using AUC, AUPR, and F1 score, GAECDS superior to other state-of-the-art models. In addition, four pairs of the predicted top 10 drug combinations were found to work well enough for evaluation. The case study shows that the GAECDS approach is useful for identifying potential drug synergy.

Somatic mutations often occur at high relapse sites in protein sequences, which indicates that the location clustering of somatic missense mutations can be used to identify driving genes. However, the traditional clustering algorithm has such problems as the background signal over-fitting, the clustering algorithm is not suitable for mutation data, and the performance of identifying low-frequency mutation genes needs to be improved. In this paper, we propose a linear clustering algorithm based on likelihood ratio test knowledge to identify driver genes. In this experiment, firstly, the polynucleotide mutation rate is calculated based on the prior knowledge of likelihood ratio test. Then, the simulation data set is obtained through the background mutation rate model. Finally, the unsupervised peak clustering algorithm is used to, respectively, evaluate the somatic mutation data and the simulation data to identify the driver genes. The experimental results show that our method achieves a better balance of precision and sensitivity. It can also identify the driver genes missed by other methods, making it an effective supplement to other methods. We also discover some potential linkages between genes and between genes and mutation sites, which is of great value to target drug therapy research. Method framework: Our proposed model framework is as follows. a. Counting mutation sites and the number of mutations in tumor gene elements. b. The nucleotide context mutation frequency is counted based on the likelihood ratio test knowledge, and the background mutation rate model is obtained. c. Based on Monte Carlo simulation method, data sets with the same number of mutations as gene elements are randomly sampled to obtain simulated mutation data, and the sampling frequency of each mutation site is related to the mutation rate of polynucleotide. d. The original mutation data and the simulated mutation data after random reconstruction are clustered by peak density, respectively, and the corresponding clustering scores are obtained. e. We can obtain the clustering information statistics in each gene segment and score of each gene segment from the original single nucleotide mutation data through step d. f. According to the observed score and the simulated clustering score, the p-value of the corresponding gene fragment is calculated. g. We can obtain the clustering information statistics in each gene segment and score of each gene segment from the simulated single nucleotide mutation data through step d.

Precise forecasting of survival risk plays a pivotal role in comprehending and predicting the prognosis of patients afflicted with esophageal squamous cell carcinoma (ESCC). The existing methods have the problems of insufficient fitting ability and poor interpretability. To address this issue, this work proposes a novel interpretable survival risk prediction method for ESCC patients based on extreme gradient boosting improved by whale optimization algorithm (WOA-XGBoost) and shapley additive explanations (SHAP). Given the imbalanced nature of the data set, the adaptive synthetic sampling (ADASYN) is first used to generate the samples with high survival risk. Then, an improved clustering by fast search and find of density peaks (IDPC) algorithm based on cosine distance and K nearest neighbors is used to cluster the patients. Next, the prediction model for each cluster is obtained by WOA-XGBoost and the constructed model is visualized with SHAP to uncover the factors hidden in the structured model and improve the interpretability of the black-box model. Finally, the effectiveness of the proposed scheme is demonstrated by analyzing the data collected from the First Affiliated Hospital of Zhengzhou University. The results of the analysis reveal that the proposed methodology exhibits superior performance, as indicated by the area under the receiver operating characteristic curve (AUROC) of 0.918 and accuracy of 0.881.

Diabetes mellitus significantly contributes to breast cancer progression, where hyperglycemia upregulates specific genes, leading to more aggressive tumor growth. In patients with BC that develop diabetes, neuregulin 1 (NRG1) and epidermal growth factor receptor 3 (ERBB3) overexpression exacerbate tumor growth and progression. Since the interaction between NRG1 and ERBB3 is critical for tumor growth, understanding the molecular mechanisms underlying NRG1-ERBB3 complex formation is essential for elucidating diabetes-assisted breast cancer progression. However, the key residues forming the NRG1-ERBB3 complex remain unknown. Here, we substituted specific residues in NRG1 with alanine and studied its interactions with ERBB3 using computational structural biology tools. We further screened the South African natural compounds database to target the complex's interface residues to discover potential inhibitors. The conformational stability and dynamic features of NRG1-WT, -H2A, -L3A, and -K35A complexed with ERBB3 were subjected to 400 ns molecular dynamics simulations. The free binding energies of all NRG1-ERBB3 complexes were calculated using the molecular mechanics-generalized Born surface area (MM/GBSA). The H2 and L3 alanine substitutions caused a loss of interaction with ERBB3 residue D73, weakening the interaction with ERBB3. Screening 1300 natural compounds identified four (SANC00643, SANC00824, SANC00975, and SANC00335) with the best potential to inhibit ERRB3-NRG1 coupling. The binding free energies for each complex were - 48.55 kcal/mol for SANC00643, - 47.68 kcal/mol for SANC00824, - 46.04 kcal/mol for SANC00975, and - 45.29 kcal/mol for SANC00335, showing their overall stronger binding with ERBB3 than NRG1 and their potential to act as ERBB3-NRG1 complex inhibitors. In conclusion, this complex may represent a residue-specific drug target to inhibit BC progression.