Anticancer drug synergy prediction based on CatBoost
Changheng Li, Nana Guan, Hongyi Zhang
TL;DR
This paper introduces a machine learning model using CatBoost to predict effective anticancer drug combinations, achieving strong performance and identifying key genes involved in drug synergy.
Contribution
A novel CatBoost-based model for predicting anticancer drug synergy with improved performance and biological insights via SHAP analysis.
Findings
The model achieved a ROC AUC of 0.9217 and outperformed three other advanced models in predicting drug synergy.
Drug features were more influential than cell line features in predicting synergy, according to SHAP analysis.
Genes like PTK2, CCND1, and GNA11 were identified as important in drug synergy prediction.
Abstract
The research of cancer treatments has always been a hot topic in the medical field. Multi-targeted combination drugs have been considered as an ideal option for cancer treatment. Since it is not feasible to use clinical experience or high-throughput screening to identify the complete combinatorial space, methods such as machine learning models offer the possibility to explore the combinatorial space effectively. In this work, we proposed a machine learning method based on CatBoost to predict the synergy scores of anticancer drug combinations on cancer cell lines, which utilized oblivious trees and ordered boosting technique to avoid overfitting and bias. The model was trained and tested using the data screened from NCI-ALMANAC dataset. The drugs were characterized with morgan fingerprints, drug target information, monotherapy information, and the cell lines were described with gene…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsComputational Drug Discovery Methods · Bioinformatics and Genomic Networks · Microbial Natural Products and Biosynthesis