All Stories

  1. QSAR: Using the Past to Study the Present
  2. Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project
  3. Big data and deep learning: extracting and revising chemical knowledge from data
  4. Informatics in Control, Automation and Robotics
  5. Active upper limb prostheses: a review on current state and upcoming breakthroughs
  6. Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics
  7. Correction to Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor
  8. Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors
  9. Predictive models of toxicity using deep learning: the case of mutagenicity
  10. Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor
  11. Can chemical similarity be computed by deep learning?
  12. Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling
  13. Discovering substructures able to predicting toxicity against fish
  14. A neuromorphic control architecture for a biped robot
  15. Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy
  16. Could deep learning in neural networks improve the QSAR models?
  17. Constructing knowledge bases for robots
  18. A robot cognitive model learns which action to select
  19. Programs that predict mutagenicity of chemical compouds and their integration
  20. Classify shoulder movements from sEMG signals
  21. Understanting the computational methods for predicting the biological properties of chemicals
  22. Advances in QSAR Modeling
  23. Comparing expert read-across predictions
  24. From learning to new goal generation in a bioinspired robotic setup
  25. New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds
  26. ToxRead: A tool to assist in read across and its use to assess mutagenicity of chemicals
  27. Molecular substructures linked to ready biodegradability of chemicals
  28. Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction
  29. CORAL: Quantitative structure-activity relationship models for estimating toxicity of organic compounds in rats
  30. Mining toxicity structural alerts from SMILES: A new way to derive Structure Activity Relationships
  31. ChemInform Abstract: The Importance of Scaling in Data Mining for Toxicity Prediction.
  32. GUEST EDITORIAL: MARCO SOMALVICO MEMORIAL ISSUE
  33. GUEST EDITORIAL: AN ARTIFICIAL INTELLIGENCE MISCELLANEA, REMEMBERING MARCO SOMALVICO
  34. Guest editorial: Marco Somalvico memorial issue
  35. GUEST EDITORIAL: PAPERS IN SENSING AND IN REASONING (MARCO SOMALVICO MEMORIAL ISSUE)
  36. E-MODELLING: FOUNDATIONS AND CASES FOR APPLYING AI TO LIFE SCIENCES
  37. Description of the Electronic Structure of Organic Chemicals Using Semiempirical and Ab Initio Methods for Development of Toxicological QSARs
  38. LARP, Biped Robotics Conceived as Human Modelling
  39. MULTICLASS CLASSIFIER FROM A COMBINATION OF LOCAL EXPERTS: TOWARD DISTRIBUTED COMPUTATION FOR REAL-PROBLEM CLASSIFIERS
  40. Database mining with adaptive fuzzy partition: Application to the prediction of pesticide toxicity on rats
  41. The Importance of Scaling in Data Mining for Toxicity Prediction
  42. Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds
  43. Robotic programs for manipulation can use a frame-based model of the world
  44. Interactive development of object handling programs
  45. Advanced steps in biped robotics: innovative design and intuitive control through spring-damper actuator
  46. Clustering and classification techniques to assess aquatic toxicity