Introduction:: Numerous clinical data and animal models demonstrate that many patients with neuropathic pain suffer from concomitant depressive symptoms. Methods:: Massive evidence from biological experiments has verified that the medial prefrontal cortex (mPFC), prefrontal cortex, hippocampus, and other brain regions play an influential role in the co-morbidity of neuropathic pain and depression, but the mechanism by which neuropathic pain induces depression remains unclear. Results:: In this study, we mined existing publicly available databases of high-throughput sequencing data intending to identify the differentially expressed genes (DEGs) in the process of neuropathic pain-induced depression. Conclusion:: This study provides a rudimentary exploration of the mechanism of neuropathic pain-induced depression and provides credible evidence for its management and precaution.

Background: New drugs are costly, time-consuming, and often accompanied by safety concerns. With the development of deep learning, computer-aided drug design has become more mainstream, and convolutional neural networks and graph neural networks have been widely used for drug–target affinity (DTA) prediction. Objective: The paper proposes a method of predicting DTA using graph convolutional networks and multiscale convolutional neural networks. Methods: We construct drug molecules into graph representation vectors and learn feature expressions through graph attention networks and graph convolutional networks. A three-branch convolutional neural network learns the local and global features of protein sequences, and the two feature representations are merged into a regression module to predict the DTA. Results: We present a novel model to predict DTA, with a 2.5% improvement in the consistency index and a 21% accuracy improvement in terms of the mean squared error on the Davis dataset compared to DeepDTA. Morever, our method outperformed other mainstream DTA prediction models namely, GANsDTA, WideDTA, GraphDTA and DeepAffinity. Conclusion: The results showed that the use of multiscale convolutional neural networks was better than a single-branched convolutional neural network at capturing protein signatures and the use of graphs to express drug molecules yielded better results.

Introduction: Angiogenesis is one of the hallmarks of cancer and can impact the processes of cancer initiation, progression, and response to therapy. Background: Anti-angiogenic therapy is thus an encouraging therapeutic option to treat cancers, but the detailed angiogenic mechanisms and the association between angiogenesis and clinical outcome remain unknown in different cancers. Methods: Here, we systematically assess the impacts of 82 angiogenesis-associated genes (AAGs) in tumor tissue specificity and prognosis across 16 cancer types. Results: Results demonstrate that the expression patterns of the 82 AAGs can reflect the tumor tissue specificity, and high expressions of up-regulated AAGs are significantly associated with poor prognosis of cancer. We further define a prognostic score for predicting overall survival (OS) based on the expressions of up-regulated AAGs and confirm its reliable predictive ability. Results indicate that a low prognostic score demonstrates a superior OS and vice versa. Conclusion: The results of this study will contribute to the understanding of different tumor angiogenesis mechanisms in various tissues and cancer-personalized anti-angiogenic treatment. The code of our analysis can be accessed at https://github.com/ZhenshenBao/AAGs_analysis.git.

Background: Over the last several decades, predicting protein structures from amino acid sequences has been a core task in bioinformatics. Nowadays, the most successful methods employ multiple sequence alignments and can predict the structure with excellent performance. These predictions take advantage of all the amino acids at a given position and their frequencies. However, the effect of single amino acid substitutions in a specific protein tends to be hidden by the alignment profile. For this reason, single-sequence-based predictions attract interest even after accurate multiple-alignment methods have become available: the use of single sequences ensures that the effects of substitution are not confounded by homologous sequences. Objective: This work aims at understanding how the single-sequence secondary structure prediction of a residue is influenced by the surrounding ones. We aim at understanding how different prediction methods use single-sequence information to predict the structure. Methods: We compare mutual information, the coefficients of two linear models, and three deep learning networks. For the deep learning algorithms, we use the DeepLIFT analysis to assess the effect of each residue at each position in the prediction. Result: Mutual information and linear models quantify direct effects, whereas DeepLIFT applied on deep learning networks quantifies both direct and indirect effects. Conclusion: Our analysis shows how different network architectures use the information of single protein sequences and highlights their differences with respect to linear models. In particular, the deep learning implementations take into account context and single position information differently, with the best results obtained using the BERT architecture.

Aim: The study aimed to develop a robust and more precise 6mA methylation prediction tool that assists researchers in studying the epigenetic behaviour of crop plants. Background: N6-methyladenine (6mA) is one of the predominant epigenetic modifications involved in a variety of biological processes in all three kingdoms of life. While in vitro approaches are more precise in detecting epigenetic alterations, they are resource-intensive and time-consuming. Artificial intelligence- based in silico methods have helped overcome these bottlenecks Methods: A novel machine learning framework was developed through the incorporation of four techniques: ensemble machine learning, hybrid approach for feature selection, the addition of features, such as Average Mutual Information Profile (AMIP), and bootstrap samples. In this study, four different feature sets, namely di-nucleotide frequency, GC content, AMIP, and nucleotide chemical properties were chosen for the vectorization of DNA sequences. Nine machine learning models, including support vector machine, random forest, k-nearest neighbor, artificial neural network, multiple logistic regression, decision tree, naïve Bayes, AdaBoost, and gradient boosting were employed using relevant features extracted through the feature selection module. The top three best-performing models were selected and a robust ensemble model was developed to predict sequences with 6mA sites. Results: EpiSemble, a novel ensemble model was developed for the prediction of 6mA methylation sites. Using the new model, an improvement in accuracy of 7.0%, 3.74%, and 6.65% was achieved over existing models for RiceChen, RiceLv, and Arabidopsis datasets, respectively. An R package, EpiSemble, based on the new model was developed and made available at https://cran.rproject. org/web/packages/EpiSemble/index.html. Conclusion: The EpiSemble model added AMIP as a novel feature, integrated feature selection modules, bootstrapping of samples, and ensemble technique to achieve an improved output for accurate prediction of 6mA sites in plants. To our knowledge, this is the first R package developed for predicting epigenetic sites of genomes in crop plants, which is expected to help plant researchers in their future explorations.

Background: The prediction of the drug-induced liver injury (DILI) of chemicals is still a key issue of the adverse drug reactions (ADRs) that needs to be solved urgently in drug development. The development of a novel method with good predictive capability and strong mechanism interpretation is still a focus topic in exploring the DILI. Objective: With the help of systems biology and network analysis techniques, a class of descriptors that can reflect the influence of drug targets in the pathogenesis of DILI is established. Then a machine learning model with good predictive capability and strong mechanism interpretation is developed between these descriptors and the toxicity of DILI. Methods: After overlapping the DILI disease module and the drug-target network, we developed novel descriptors according to the number of drug genes with different network overlapped distance parameters. The hepatotoxicity of drugs is predicted based on these novel descriptors and the classical molecular descriptors. Then the DILI mechanism interpretations of drugs are carried out with important network topological descriptors in the prediction model. Results: First, we collected targets of drugs and DILI-related genes and developed 5 NT parameters (S, Nds=0, Nds=1, Nds=2, and Nds>2) based on their relationship with a DILI disease module. Then hepatotoxicity predicting models were established between the above NT parameters combined with molecular descriptors and drugs through the machine learning algorithms. We found that the NT parameters had a significant contribution in the model (ACCtraining set=0.71, AUCtraining set=0.76; ACCexternal set=0.79, AUCexternal set=0.83) developed by these descriptors within the applicability domain, especially for Nds=2, and Nds>2. Then, the DILI mechanism of acetaminophen (APAP) and gefitinib are explored based on their risk genes related to ds=2. There are 26 DILI risk genes in the regulation of cell death regulated with two steps by 5 APAP targets, and gefitinib regulated risk gene of CLDN1, EIF2B4, and AMIGO1 with two steps led to DILI which fell in the biological process of response to oxygen-containing compound, indicating that different drugs possibly induced liver injury through regulating different biological functions. Conclusion: A novel method based on network strategies and machine learning algorithms successfully explored the DILI of drugs. The NT parameters had shown advantages in illustrating the DILI mechanism of chemicals according to the relationships between the drug targets and the DILI risk genes in the human interactome. It can provide a novel candidate of molecular descriptors for the predictions of other ADRs or even of the predictions of ADME/T activity.

Backgroud: PIWI-interacting RNAs (piRNAs) are a kind of small non-coding RNAs which interact with PIWI proteins and play a vital role in safeguarding genome. Single nucleotide polymorphisms (SNPs) are widely distributed variations which are associated with diseases and have rich information. Up to now, various studies have proved that SNPs on piRNA were related to diseases. Objective: In order to create a comprehensive source about piRNA-related SNPs, we developed a publicly available online database piRSNP. Methods: We systematically identified SNPs on human and mouse piRNAs. piRSNP contains 42,967,522 SNPs on 10,773,081 human piRNAs and 29,262,185 SNPs on 16,957,706 mouse piRNAs. Results: 7,446 SNPs on 519 cancer-related piRNAs and their flanks are investigated. Impacts of 2,512 variations of cancer-related piRNAs on piRNA-mRNA interactions are analyzed. Conclusion: All these useful data and piRNA expression profiles of 12 cancer types in both tumor and pericarcinomatous tissues are compiled into piRSNP. piRSNP characterizes human and mouse piRNArelated SNPs comprehensively and could be beneficial for researchers to investigate subsequent piRNA functions. Database URL is http://www.ibiomedical.net/piRSNP/.

Introduction: The discovery of tumor subtypes helps to explore tumor pathogenesis, determine the operability of clinical treatment, and improve patient survival. Clustering analysis is increasingly applied to multi-genomic data. However, due to the diversity and complexity of multi-omics data, developing a complete clustering algorithm for tumor molecular typing is still challenging. Methods: In this study, we present an adaptive density-aware spectral clustering method based on a variational autoencoder (ADSVAE). ADSVAE first learns the underlying spatial information of each omics data using a variational autoencoder (VAE) based on the Wasserstein distance metric. Secondly, a similarity matrix is built for each gene set using an adaptive density-aware kernel. Thirdly, tensor product graphs (TPGs) are used to merge different data sources and reduce noise. Finally, ADSVAE employs a spectral clustering algorithm and utilizes the Gaussian mixture model (GMM) to cluster the final eigenvector matrix to identify cancer subtypes. Results: We tested ADSVAE on 5 TCGA datasets, all with good performance in comparison with several advanced multi-omics clustering algorithms. Compared with the existing multi-group clustering algorithms, the variational autoencoder based on the Wasserstein distance measure in the ADSVAE algorithm can learn the underlying spatial information on each omics data, which has a better effect on learning complex data distribution. The self-tuning density-aware kernel used by the ADSVAE algorithm enhances the similarity between shared near neighbor points, and the process of tensor product plot data integration and diffusion can better reduce the noise and reveal the underlying structure, improving the performance. Conclusion: Due to the inherent pitfalls of computational biology in the study of cancer subtype identification, although some research conclusions have been made in this paper on the related issues, as the research in related fields continues to deepen, the clustering study of cancer subtype identification based on genomic data needs further improvement and refinement.

Background: Careful consideration is required for detecting significant features (proteins or peptides) in LC-MS proteomics studies using multivariable regression analyses. In proteomics data, missing values can arise due to random errors, bad samples, features below the detection limit in specific samples, etc. Further, expression data are always prone to heterogeneity due to technical/biological reasons. Missing values and heterogeneity in proteomics studies can confound important findings. Moreover, there is additional information in these studies, such as pre-clinical and clinical information (e.g., sex, exposure, etc.), which can be used to supplement the inference. Methods: We introduce a user-friendly web application SATP (Statistical Analysis interactive Tool for label-free LC-MS Proteomics experiments) for differential expression analysis of proteomics data that is scalable to large clinical proteomic studies. Appropriate normalization and imputation methods have been provided. Apart from these, several statistical tests such as t-test, moderated t-test, linear fixed effect model, and linear mixed model with adjustment of effect of extra covariates have also been provided for users' benefit. Results: Our intuitive tool has several advantages over the existing ones, including an extension to multiple factor comparisons after adjusting for covariates. Conclusion: This is a comprehensive tool for analysis of complex experiments with multiple covariates, whereas most of the existing tools were developed for comparing simple experiments mostly with two groups without covariates. Availability: The tool can be accessed freely by the users from https://ulbbf.shinyapps.io/satp/.

Background: Single-cell RNA sequencing is an advanced technology that makes it possible to unravel cellular heterogeneity and conduct single-cell analysis of gene expression. However, owing to technical defects, many dropout events occur during sequencing, bringing about adverse effects on downstream analysis. Methods: To solve the dropout events existing in single-cell RNA sequencing, we propose an imputation method scTSSR-D, which recovers gene expression by two-side self-representation and dropout information. scTSSR-D is the first global method that combines a partial imputation method to impute dropout values. In other words, we make full use of genes, cells, and dropout information when recovering the gene expression. Results: The results show scTSSR-D outperforms other existing methods in the following experiments: capturing the Gini coefficient and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization, down-sampling experiments, differential expression analysis, and the accuracy of cell clustering. Conclusion: scTSSR-D is a more stable and reliable method to recover gene expression. Meanwhile, our method improves even more dramatically on large datasets compared to the result of existing methods.