The accurate prediction of chemical toxicity remains a major challenge in drug development and chemical safety assessment because conventional approaches can be costly, time-consuming, ethically constrained, and limited in predictive performance. This review examines how artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), is being applied to predictive toxicology. It covers conventional algorithms such as support vector machines (SVMs), decision trees (DTs), and ensemble methods, together with advanced approaches including graph neural networks (GNNs). The review evaluates AI-based methods across five organ-specific toxicity domains—hepatotoxicity, cardiotoxicity, respiratory toxicity, nephrotoxicity, and neurotoxicity—and critically compares molecular representations, data-integration strategies, model architectures, validation protocols, and interpretability approaches. Particular emphasis is placed on data quality, class imbalance, model explainability, applicability domains, external validation, and regulatory acceptance. By integrating recent methodological advances with persistent implementation challenges, this review provides a technical framework for developing more transparent, reliable, and generalizable computational tools for safety assessment in drug discovery and environmental health.
Subash M, Sheik Asraf S, Santhanamari M, Nagaraj P, Ayodeji Olalekan Salau, Abdullahi Garba Usman, Mouaz Yousfan
Rheumatoid arthritis (RA) is one of the autoimmune diseases that involve joint damage and synovial membrane inflammation. There have been many attempts to examine the utility of biomarkers in the diagnosis of RA, but there is no clinically accepted biomarker for the diagnosis of RA due to its heterogeneity. Thus, in this study, we tried to find RA biomarkers using machine learning and bioinformatics approaches. Three corresponding gene expression datasets (GSE55457, GSE12021, and GSE77298) were downloaded from the Gene Expression Omnibus database. Batch effect correction was carried out using the ComBat function of the sva package in R. Differentially expressed genes (DEGs) compared to controls were identified using limma. To identify the biological functions and pathways of DEGs, Gene Ontology, KEGG, and gene set enrichment analysis (GSEA) were conducted. The protein–protein interaction (PPI) network was constructed in Cytoscape using the STRING database. Genes identified through PPI analysis and further filtered by LASSO and SVM-RFE regressions were considered as diagnostic biomarkers. Additional analysis of hub genes was conducted using the GSE89408 dataset. CIBERSORTx was used to evaluate the immune cell infiltration in RA. A total of 440 DEGs were selected, including 197 downregulated and 243 upregulated genes. The DEGs were involved in biological processes and pathways associated with the immune system and were suggested by functional enrichment analysis. GSEA enriched in cytokine–cytokine receptor interaction, hematopoietic cell lineage, primary immunodeficiency, and RA pathways. The hub genes CXCL13 and CXCL10 were chosen with the help of a machine learning algorithm. An external independent validation of the hub genes was conducted. In addition, ROC analysis showed a good diagnostic value of the two genes. The analysis of immune cell infiltration showed CXCL13 was associated with multiple immune-cell populations, including plasma cells, T-cell subsets, and M1 macrophages, whereas CXCL10 showed significant associations with M1 macrophages, plasma cells, and selected T-cell subsets. Correlation analysis showed a high correlation between chemokines CXCL13 and CXCL10 and many infiltrated immune cells. The present study indicates the potential usefulness of CXCL13 and CXCL10 genes as diagnostic biomarkers in RA. Synovial inflammation in RA is associated with the coordinated action of many genes involved in the recruitment of various immune cells to the inflammatory site. Although the term chemokines were used to designate them, these genes are the genes encoding molecules associated with the migration of particular immune cell types, and CXCR5 and CXCR3 may be considered the potential targets for future research.
This article draws on the authors’ more than 17 years of experience in transcriptomic research, beginning with the introduction of high-throughput sequencing in Mexico in 2009. Here, we provide a critical review of bioinformatics pipelines for different RNA classes, ranging from small RNAs to long non-coding RNAs. Our analysis encompasses a range of scenarios, from de novo transcriptome assembly for organisms lacking a closely related and adequately annotated reference genome to differential expression analysis based on a well-annotated reference genome. By providing a comprehensive overview of the workflow, from RNA extraction and high-throughput sequencing to quality control and data preprocessing, we offer a practical framework to help researchers select tools that are appropriate for their biological questions and experimental contexts. We further examine advanced strategies for transcriptome assembly, sequence alignment, and transcript abundance quantification. Finally, we discuss differential expression and functional enrichment analyses, emphasizing the importance of standardized, FAIR-compliant workflows and the democratization of transcriptomic analysis in promoting inclusive and reproducible genomic research. We also present IDEAMEX, a web-based platform designed to democratize transcriptomic analysis by reducing technical and systemic barriers and promoting equitable access to robust analytical tools, particularly for researchers in resource-limited settings who may not have advanced programming skills.
This research aimed to identify novel oxidative stress (OS)–related biomarkers and develop predictive models for the early detection of pediatric sepsis. Five open-access datasets from the gene expression omnibus database were analyzed, which included peripheral whole blood samples from children with sepsis and healthy controls. Using bioinformatics approaches, we identified differentially expressed genes (DEGs) associated with OS. We then combined weighted gene co-expression network analysis (WGCNA) and multiple machine learning algorithms (ML) to identify seven candidate genes, constructed a protein–protein interaction (PPI) network to screen six hub genes, and evaluated their correlations with the immune microenvironment. We subsequently evaluated the diagnostic performance of the hub genes using training datasets (GSE26440 and GSE26378) and validation datasets (GSE13904, GSE8121, and GSE9692). Moreover, molecular docking analysis revealed interactions between hub gene–encoded proteins and small-molecule compounds, which helps predict drug sensitivity. The study identified 36 differentially expressed oxidative stress-related genes (OSRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses highlighted these genes’ roles in regulating inflammatory responses and reactive oxygen species metabolism. Seven candidate genes were identified using machine learning algorithms. Subsequently, through protein-protein interaction (PPI) network analysis, six hub genes, namely, GADD45A, HP, MAPK14, PGD, RETN, and S100A9, were identified as having significant diagnostic potential. Immune cell deconvolution analysis of peripheral blood transcriptomes suggested that hub genes related to oxidative stress may be linked to alterations in the composition of peripheral blood immune cells. The diagnostic performance of the six hub genes was validated with area under the curve (AUC) values between 0.908 and 0.982, showing their promising diagnostic performance. Additionally, molecular docking analysis suggested that small molecules Doxycycline, Ralimetinib mesylate, Tasquinimod might interact with the identified hub genes, which opens up new treatment options. Our research not only reveals potential diagnostic biomarkers of pediatric sepsis but also sets the stage for future studies. These studies aim to translate our findings into clinical practice, enabling earlier and more accurate diagnoses as well as targeted therapies to improve outcomes for children.
The process of DNA transcription leads to the generation of torsional stress, which must be resolved for smooth progression of the transcription machinery. In Saccharomyces cerevisiae, DNA topoisomerase I (Top I), a type IB topoisomerase, plays a critical role in relaxing supercoils and mitigating the topological strain associated with transcription. While several proteins from the transcription machinery have been reported to interact with yeast Top I, the precise structural domains involved and the detailed features of the interaction interfaces remain largely unexplored. This gap is partly due to the absence of a complete three-dimensional structure of the full-length enzyme, which hinders structure-based computational analyses of its interactome. A full-length model of yeast Top I was generated using a domain-based modeling approach and evaluated using structural validation metrics and comparative analyses with homologous structures. Leveraging this model, we investigated a potential interaction between Top I and Rpc82, a key subunit of RNA polymerase III involved in transcription of small non-coding RNAs such as tRNAs and 5S rRNA. The interaction was evaluated using an integrative computational approach combining molecular docking, binding free-energy calculations, and molecular dynamics simulations. The analyses identified a putative interaction interface involving the WH2–WH3 region of Rpc82 and primarily the D1 and D3 domains of Top I. Molecular dynamics simulations indicated that the predicted Top I–Rpc82 complex remained structurally stable under the simulated conditions. The calculated binding energy-to-surface-area ratio of the complex was comparable to benchmark values for biologically relevant heterodimeric protein interfaces, supporting the plausibility of the predicted interaction. Overall these findings provide a structural framework for future experimental validation of Top I interactions, paving the way for mechanistic insights into its regulatory functions and contribution to genome maintenance and stability.
