PWN: enhanced random walk on a warped network for disease target prioritization

Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based appro...

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Bibliographic Details
Published in:	BMC bioinformatics Vol. 24; no. 1; pp. 105 - 21
Main Authors:	Han, Seokjin, Hong, Jinhee, Yun, So Jeong, Koo, Hee Jung, Kim, Tae Yong
Format:	Journal Article
Language:	English
Published:	England BioMed Central Ltd 21-03-2023 BioMed Central BMC
Subjects:	Algorithms Cancer Computational Biology - methods Data analysis Data mining Disease Disease-target identification Drug discovery Drug Discovery - methods Machine learning Medical research Medicine, Experimental Methods Performance enhancement Protein-protein interactions Proteins Protein–protein interaction Random walk Random walks (Mathematics) United Kingdom Random walk Protein–protein interaction Disease-target identification Machine learning
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Summary:	Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks. We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge. We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-023-05227-x