Drug Response Prediction as a Link Prediction Problem

Abstract Drug response prediction is a well-studied problem in which the molecular profile of a given sample is used to predict the effect of a given drug on that sample. Effective solutions to this problem hold the key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features to develop machine learning models predictive of drug response. Molecular networks provide a functional context for the integration of genomic features, thereby resulting in robust and reproducible predictive models. However, inclusion of network data increases dimensionality and poses additional challenges for common machine learning tasks. To overcome these challenges, we here formulate drug response prediction as a link prediction problem. For this purpose, we represent drug response data for a large cohort of cell lines as a heterogeneous network. Using this network, we compute “network profiles” for cell lines and drugs. We then use the associations between these profiles to predict links between drugs and cell lines. Through leave-one-out cross validation and cross-classification on independent datasets, we show that this approach leads to accurate and reproducible classification of sensitive and resistant cell line-drug pairs, with 85% accuracy. We also examine the biological relevance of the network profiles.

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Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽

10.3390/e23060664 ◽

2021 ◽

Vol 23 (6) ◽

pp. 664

Author(s):

Nikos Kanakaris ◽

Nikolaos Giarelis ◽

Ilias Siachos ◽

Nikos Karacapilidis

Keyword(s):

Language Processing ◽

Scientific Knowledge ◽

Link Prediction ◽

Performance Metrics ◽

Future Research ◽

Knowledge Graph ◽

Prediction Problem ◽

Textual Information ◽

Research Collaborations ◽

Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

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Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽

10.3390/math9070772 ◽

2021 ◽

Vol 9 (7) ◽

pp. 772

Author(s):

Seonghun Kim ◽

Seockhun Bae ◽

Yinhua Piao ◽

Kyuri Jo

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Large Scale ◽

Drug Response ◽

Response Prediction ◽

Biological Data ◽

Expression Data ◽

Convolutional Network ◽

Essential Information ◽

Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

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A showcase study on personalized in silico drug response prediction based on the genetic landscape of muscle invasive bladder cancer

Scientific Reports ◽

10.1038/s41598-021-85151-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Friedemann Krentel ◽

Franziska Singer ◽

María Lourdes Rosano-Gonzalez ◽

Ewan A. Gibb ◽

Yang Liu ◽

...

Keyword(s):

Bladder Cancer ◽

In Silico ◽

Drug Response ◽

Gene Interaction ◽

Response Prediction ◽

Manual Curation ◽

Genetic Landscape ◽

Promising Treatment ◽

Muscle Invasive ◽

The Individual

AbstractImproved and cheaper molecular diagnostics allow the shift from “one size fits all” therapies to personalised treatments targeting the individual tumor. However, the wealth of potential targets based on comprehensive sequencing remains a yet unsolved challenge that prevents its routine use in clinical practice. Thus, we designed a workflow that selects the most promising treatment targets based on multi-omics sequencing and in silico drug prediction. In this study we demonstrate the workflow with focus on bladder cancer (BLCA), as there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. Within the TCGA-BLCA cohort, our workflow identified a panel of 21 genes and 72 drugs that suggested personalized treatment for 95% of patients—including five genes not yet reported as prognostic markers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions. We discuss potential improvements of drug-gene interaction databases on the basis of pitfalls that were identified during manual curation.

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