2021
DOI: 10.1101/2021.09.12.459579
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Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Abstract: Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applicati… Show more

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Cited by 2 publications
(2 citation statements)
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“…P rior CD [145] makes use of a network propagation algorithm and a drug–drug similarity network, along with pathway activity profiles to prioritise candidate drugs in cancer. Similarly, RP ath [146] relies on a knowledge graph built from disease, protein and drug causal relations along with disease and perturbed expression signatures to prioritise compounds for a given disease.…”
Section: Transcriptomics‐based Drug Selectionmentioning
confidence: 99%
“…P rior CD [145] makes use of a network propagation algorithm and a drug–drug similarity network, along with pathway activity profiles to prioritise candidate drugs in cancer. Similarly, RP ath [146] relies on a knowledge graph built from disease, protein and drug causal relations along with disease and perturbed expression signatures to prioritise compounds for a given disease.…”
Section: Transcriptomics‐based Drug Selectionmentioning
confidence: 99%
“…and drug perturbation profiling [8] have generated a wealth of molecular data characterizing large numbers of cell lines and their responses to perturbations. Many computational approaches have been developed to leverage these molecular data for drug sensitivity predictions and/or drug repurposing [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], and these efforts have successfully identified drugs for a wide variety of diseases, including HIV [18], osteoporosis [26], diabetes [27], and cancer [14,28,29]. Despite these successes, many patients remain ineligible for targeted therapies, including over 80% of cancer patients [30].…”
Section: Introductionmentioning
confidence: 99%