Novel machine learning model for predicting cancer drugs’ susceptibilities and discovering novel treatments
KRS outperforms competitors in prediction performance and selects feature genes highly correlated with drugs' susceptibilities. Novel biological results are found by investigating KRS's feature genes.
Authors
- Xuekui Zhang (ORCID: https://orcid.org/0000-0003-4728-2343)
- He Li (ORCID: https://orcid.org/0000-0003-1152-5817)
- Junhua Gu (ORCID: https://orcid.org/0000-0001-8896-1735)
- Li Xing (ORCID: https://orcid.org/0000-0002-3332-5907)
- Hua He (ORCID: https://orcid.org/0000-0001-8461-922X)
- Yao Dong
- Xiaowen Cao (ORCID: https://orcid.org/0009-0005-0938-0032)
- Hao Ding (ORCID: https://orcid.org/0000-0002-8205-0487)
- Yushan Hu
Institutions
- Hebei University of Technology (CN)
- University of Victoria (CA)
- University of Saskatchewan (CA)
Publication Details
- Journal
- Journal of Biomedical Informatics
- Published
- 2024-12-12
- DOI
- https://doi.org/10.1016/j.jbi.2024.104762
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Michael Smith Health Research BC
- Canada Research Chairs
- National Natural Science Foundation of China
- China Scholarship Council
- Natural Science Foundation of Tianjin City
- Natural Sciences and Engineering Research Council of Canada