All Stories

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