All Stories

  1. Computational identification of preservatives with potential neuronal cytotoxicity
  2. Genome‐wide DNA methylation analysis using MethylCap‐seq in canine high‐grade B‐cell lymphoma
  3. Prediction of human fetal–maternal blood concentration ratio of chemicals
  4. Identification of Time-Invariant Biomarkers for Non-Genotoxic Hepatocarcinogen Assessment
  5. Potential Therapeutic Agents for COVID-19 Based on the Analysis of Protease and RNA Polymerase Docking
  6. Potential Therapeutic Agents for COVID-19 Based on the Analysis of Protease and RNA Polymerase Docking
  7. Potential Risk of Higenamine Misuse in Sports: Evaluation of Lotus Plumule Extract Products and a Human Study
  8. Leveraging complementary computational models for prioritizing chemicals of developmental and reproductive toxicity concern: an example of food contact materials
  9. Curation of cancer hallmark-based genes and pathways for in silico characterization of chemical carcinogenesis
  10. Transfer learning for predicting human skin sensitizers
  11. ChemDIS-Mixture: an online tool for analyzing potential interaction effects of chemical mixtures
  12. Discovery of Pyrazolo[4,3-c]quinolines Derivatives as Potential Anti-Inflammatory Agents through Inhibiting of NO Production
  13. Mechanism-informed read-across assessment of skin sensitizers based on SkinSensDB
  14. ChemDIS 2: an update of chemical-disease inference system
  15. Discovery of Indeno[1,2-c]quinoline Derivatives as Potent Dual Antituberculosis and Anti-Inflammatory Agents
  16. Profiling transcriptomes of human SH-SY5Y neuroblastoma cells exposed to maleic acid
  17. SkinSensDB: a curated database for skin sensitization assays
  18. Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens
  19. Attenuation of antigen-specific T helper 1 immunity by Neolitsea hiiranensis and its derived terpenoids
  20. ChemDIS: a chemical–disease inference system based on chemical–protein interactions
  21. Developing a QSAR model for hepatotoxicity screening of the active compounds in traditional Chinese medicines
  22. Public Databases of Plant Natural Products for Computational Drug Discovery
  23. Interpretable prediction of non-genotoxic hepatocarcinogenic chemicals
  24. An in silico toxicogenomics approach for inferring potential diseases associated with maleic acid
  25. A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model
  26. Databases for T-Cell Epitopes
  27. Acquiring Decision Rules for Predicting Ames-Negative Hepatocarcinogens Using Chemical-Chemical Interactions
  28. Rule-Based Knowledge Acquisition Method for Promoter Prediction in Human andDrosophilaSpecies
  29. Prediction of pupylation sites using the composition of k-spaced amino acid pairs
  30. Prediction and Analysis of Antibody Amyloidogenesis from Sequences
  31. Prediction of Non-genotoxic Hepatocarcinogenicity Using Chemical-Protein Interactions
  32. Identification of Biomarkers for Esophageal Squamous Cell Carcinoma Using Feature Selection and Decision Tree Methods
  33. TIPdb: A Database of Anticancer, Antiplatelet, and Antituberculosis Phytochemicals from Indigenous Plants in Taiwan
  34. PupDB: a database of pupylated proteins
  35. POPISK: T-cell reactivity prediction using support vector machines and string kernels
  36. Human Pol II promoter prediction by using nucleotide property composition features
  37. Predicting protein subnuclear localization using GO-amino-acid composition features
  38. ProLoc-rGO: Using rule-based knowledge with Gene Ontology terms for prediction of protein subnuclear localization
  39. ProLoc-GO: Utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization
  40. Computational identification of ubiquitylation sites from protein sequences
  41. ProLoc: Prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features
  42. POPI: predicting immunogenicity of MHC class I binding peptides by mining informative physicochemical properties