Skip to content
HaqHayTech Limited homeHaqHayTech Limited home
Search

Publications

13 of 24 publications

  1. Adeagbo, M. A., Agbaje, H. A., & Silveira, S. A. (2026). Protein-novelty, sequence-similarity, and negative-sampling controls reveal benchmark-specific discrimination in protein–protein interaction prediction. bioRxiv. https://doi.org/10.64898/2026.10.06.757125 Details of Protein-novelty, sequence-similarity, and negative-sampling controls reveal benchmark-specific discrimination in protein–protein interaction prediction
  2. Adeagbo, M. A., Ibrahim, W. A., & de Azevedo Silveira, S. (2026). Integrating multi-criteria decision-making and machine learning for fingerprint selection and virtual screening of Acetylcholinesterase inhibitors. Journal of Cheminformatics. https://doi.org/10.1186/s13321-026-01300-8 Details of Integrating multi-criteria decision-making and machine learning for fingerprint selection and virtual screening of Acetylcholinesterase inhibitors
  3. Adeagbo, M. A., Ibrahim, W. A., & Ortil, M. (2026). Temporal Generalization and Global Stability of Resource Importance in Software Developer Compensation Models in Stack Overflow Surveys. Science of Computer Programming. Details of Temporal Generalization and Global Stability of Resource Importance in Software Developer Compensation Models in Stack Overflow Surveys
  4. Adeagbo, M. A., & Silveira, S. A. (2025, November 25). Predicting Protein–Protein Interaction Types Using Graph-Based Surface Residue and Machine Learning. In 2ª Semana da Inteligência Artificial do IDATA, Universidade Federal de Viçosa, Brazil. Details of Predicting Protein–Protein Interaction Types Using Graph-Based Surface Residue and Machine Learning
  5. Akinsola, J. E. T., Olatunbosun, M. A., Olaniyi, I. M., Adeagbo, M. A., Olajubu, E. A., & Aderounmu, G. A. (2025). Application of Artificial Intelligence on MNIST Dataset for Handwritten Digit Classification for Evaluation of Deep Learning Models. Acadlore Transactions on AI and Machine Learning, 4(3), 219–234. https://doi.org/10.56578/ataiml040305 Details of Application of Artificial Intelligence on MNIST Dataset for Handwritten Digit Classification for Evaluation of Deep Learning Models