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Ensembled machine learning framework for drug sensitivity prediction
1CSED, T.I.E.T, Punjab, Patiala, India. amans.3008@gmail.com.
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.
Insights
This study introduces an improved ensemble learning framework for predicting anti-cancer drug responses. The novel approach enhances drug sensitivity prediction accuracy, outperforming existing methods.
Area of Science:
- Computational biology
- Pharmacogenomics
- Machine learning in oncology
Background:
- Drug sensitivity prediction is crucial for drug design and discovery.
- Cancer patient responses to therapy are highly heterogeneous.
- Existing computational methods for drug sensitivity prediction lack sufficient efficiency.
Purpose of the Study:
- To develop an advanced ensemble learning framework for accurate drug-response prediction.
- To evaluate the proposed framework against state-of-the-art algorithms and baseline methods.
- To assess the framework's potential in predicting missing drug response values.
Main Methods:
- An ensemble learning framework utilizing a modified rotation forest was developed.
- The framework was tested using drug sensitivity data from Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE).
- Performance was compared against three state-of-the-art algorithms and two baseline methods.
Main Results:
- The proposed framework demonstrated superior performance compared to other methods.
- An average mean square error of 3.14 (GDSC) and 0.404 (CCLE) was achieved.
- The approach successfully predicted missing drug response values without using gene mutation data.
Conclusions:
- The developed ensemble learning framework shows significant potential for improving anti-cancer drug response prediction.
- This method offers a promising computational tool for precision oncology.
- Further research could explore incorporating gene mutation data to enhance predictive accuracy.
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