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.

The Mediator complex plays an essential role in the regulation of eukaryotic transcription. The Saccharomyces cerevisiae core Mediator comprises 21 subunits, which are organized into Head, Middle and Tail modules. Previously, the Head module was assigned to a distinct dense domain at the base, and the Middle and Tail modules were identified to form a tight structure above the Head module, which apparently contradicted findings from many biochemical and functional studies. Here, we compared the structures of the core Mediator and its subcomplexes, especially the first 3D structure of the Head + Middle modules, which permitted an unambiguous assignment of the three modules. Furthermore, nanogold labeling pinpointing four Mediator subunits from different modules conclusively validated the modular assignment, in which the Head and Middle modules fold back on one another and form the upper portion of the core Mediator, while the Tail module forms a distinct dense domain at the base. The new modular model of the core Mediator has reconciled the previous inconsistencies between the structurally and functionally defined Mediator modules. Collectively, these analyses completely redefine the modular organization of the core Mediator, which allow us to integrate the structural and functional information into a coherent mechanism for the Mediator's modularity and regulation in transcription initiation.

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Motivation Prediction of cancer patient’s response to therapeutic agent is important for personalized treatment. Because experimental verification of reactions between large cohort of patients and drugs is time-intensive, expensive and impractical, preclinical prediction model based on large-scale pharmacogenomic of cancer cell line is highly expected. However, most of the existing computational studies are primarily based on genomic profiles of cancer cell lines while ignoring relationships among genes and failing to capture functional similarity of cell lines. Results In this study, we present a novel approach named NRL2DRP, which integrates protein–protein interactions and captures similarity of cell lines’ functional contexts, to predict drug responses. Through integrating genomic aberrations and drug responses information with protein–protein interactions, we construct a large response-related network, where the neighborhood structure of cell line provides a functional context to its therapeutic responses. Representation vectors of cell lines are extracted through network representation learning method, which could preserve vertices’ neighborhood similarity and serve as features to build predictor for drug responses. The predictive performance of NRL2DRP is verified by cross-validation on GDSC dataset and methods comparison, where NRL2DRP achieves AUC > 79% for half drugs and outperforms previous methods. The validity of NRL2DRP is also supported by its effectiveness on uncovering accurate novel relationships between cell lines and drugs. Lots of newly predicted drug responses are confirmed by reported experimental evidences. Availability and implementation The code and documentation are available on https://github.com/USTC-HIlab/NRL2DRP. Supplementary information Supplementary data are available at Bioinformatics online.

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