Ensembled machine learning framework for drug sensitivity prediction. Issue 1 (1st February 2020)

• Record Type: Journal Article
• Authors: Sharma, Aman; Rani, Rinkle
• Is Part Of: IET systems biology. Volume 14:Issue 1(2020)
• Page Start: 39
• Page End: 46
• Publication Date: 2020-02-01
• Subjects: medical computing -- molecular biophysics -- genomics -- genetics -- learning (artificial intelligence) -- patient treatment -- drugs -- cellular biophysics -- cancer -- biology computing -- tumours -- diseases; ensembled machine learning framework -- drug sensitivity prediction -- drug therapy -- ensemble learning framework -- drug‐response prediction -- Cancer Cell Line Encyclopedia drug screens -- drug response values -- CCLE drug screens -- anti‐cancer drug response prediction; Systems biology -- Periodicals; Cell physiology -- Periodicals; Biological systems -- Mathematical models -- Periodicals; Genetics -- Mathematical models -- Periodicals; Computational biology -- Periodicals; 573
• DOI: 10.1049/iet-syb.2018.5094
• Languages: English
• ISSNs: 1751-8849

Abstract

Abstract : Drug sensitivity prediction is one of the critical tasks involved in drug designing and discovery. Recently several online databases and consortiums have contributed to providing open access to pharmacogenomic data. These databases have helped in developing computational approaches for drug sensitivity prediction. Cancer is a complex disease involving the heterogeneous behaviour of same tumour‐type patients towards the same kind of drug therapy. Several methods have been proposed in the literature to predict drug sensitivity. However, these methods are not efficient enough to predict drug sensitivity. The present study has proposed an ensemble learning framework for drug‐response prediction using a modified rotation forest. The proposed framework is further compared with three state‐of‐the‐art algorithms and two baseline methods using Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) drug screens. The authors have also predicted missing drug response values in the data set using the proposed approach. The proposed approach outperforms other counterparts even though gene mutation data is not incorporated while designing the approach. An average mean square error of 3.14 and 0.404 is achieved using GDSC and CCLE drug screens, respectively. The obtained results show that the proposed framework has considerable potential to improve anti‐cancer drug response prediction.