A novel heterogeneous network-based method for drug response prediction in cancer cell lines

Abstract

An enduring challenge in personalized medicine lies in selecting a suitable drug for each individual patient. Here we concentrate on predicting drug responses based on a cohort of genomic, chemical structure, and target information. Therefore, a recently study such as GDSC has provided an unprecedented opportunity to infer the potential relationships between cell line and drug. While existing approach rely primarily on regression, classification or multiple kernel learning to predict drug responses. Synthetic approach indicates drug target and protein-protein interaction could have the potential to improve the prediction performance of drug response. In this study, we propose a novel heterogeneous network-based method, named as HNMDRP, to accurately predict cell line-drug associations through incorporating heterogeneity relationship among cell line, drug and target. Compared to previous study, HNMDRP can make good use of above heterogeneous information to predict drug responses. The validity of our method is verified not only by plotting the ROC curve, but also by predicting novel cell line-drug sensitive associations which have dependable literature evidences. This allows us possibly to suggest potential sensitive associations among cell lines and drugs. Matlab and R codes of HNMDRP can be found at following https://github.com/USTC-HIlab/HNMDRP.

Toward Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-Based Convolutional Encoders

“…In addition to genomic and chemical features, previous studies have demonstrated the value of complementing drug sensitivity prediction models with prior knowledge in the form of protein-protein interactions (PPI) networks (Oskooei et al, 2018b). For example, in a network-based per-drug approach integrating these data sources, Zhang et al (2018a) surpassed various earlier models and reported a performance drop of 3.6% when excluding PPI information. However, all previous attempts at incorporating chemical information in drug sensitivity prediction rely on molecular fingerprints as chemical descriptors.…”

Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks

“…An example is molecular medicine, where analogue concepts are used to study gene association (e.g. van Laarhoven & Marchiori, ; Menden et al, ; Yamanishi, Araki, Gutteridge, Honda, & Kanehisa, ; Zhang, Wang, Xi, Yang, & Li, ) or harmful drug–drug interactions (e.g. Cheng & Zhao, ; Tari, Anwar, Liang, Cai, & Baral, ).…”

Image-Based Network Analysis of DNp73 Expression by Immunohistochemistry in Rectal Cancer Patients

“…In fact, network analysis in graph theory has been increasingly recognized as a useful tool for studying cancer. Such studies include the prediction of outcomes of ovarian cancer treatment (Zhang et al, 2013), analysis of breast cancer progression and reversal (Parikh et al, 2014), drug response prediction in cancer cell lines (Zhang et al, 2018), identification of novel cancer gene candidates (Josef Gladitz et al, 2018), tumor biology for precision cancer medicine (Ozturk et al, 2018), and prediction of cancer recurrence (Ruan et al, 2019).…”