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TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain / Yan Wang, Zuheng Xia, JINGJING DENG, Xianghua Xie, Maoguo Gong, Xiaoke Ma

BMC Bioinformatics, Volume: 22, Issue: S9

Swansea University Authors: JINGJING DENG, Xianghua Xie

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Abstract

BackgroundGene prioritization (gene ranking) aims to obtain the centrality of genes, which is critical for cancer diagnosis and therapy since keys genes correspond to the biomarkers or targets of drugs. Great efforts have been devoted to the gene ranking problem by exploring the similarity between c...

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Published in: BMC Bioinformatics
ISSN: 1471-2105
Published: Springer Science and Business Media LLC 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa57704
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Abstract: BackgroundGene prioritization (gene ranking) aims to obtain the centrality of genes, which is critical for cancer diagnosis and therapy since keys genes correspond to the biomarkers or targets of drugs. Great efforts have been devoted to the gene ranking problem by exploring the similarity between candidate and known disease-causing genes. However, when the number of disease-causing genes is limited, they are not applicable largely due to the low accuracy. Actually, the number of disease-causing genes for cancers, particularly for these rare cancers, are really limited. Therefore, there is a critical needed to design effective and efficient algorithms for gene ranking with limited prior disease-causing genes.ResultsIn this study, we propose a transfer learning based algorithm for gene prioritization (called TLGP) in the cancer (target domain) without disease-causing genes by transferring knowledge from other cancers (source domain). The underlying assumption is that knowledge shared by similar cancers improves the accuracy of gene prioritization. Specifically, TLGP first quantifies the similarity between the target and source domain by calculating the affinity matrix for genes. Then, TLGP automatically learns a fusion network for the target cancer by fusing affinity matrix, pathogenic genes and genomic data of source cancers. Finally, genes in the target cancer are prioritized. The experimental results indicate that the learnt fusion network is more reliable than gene co-expression network, implying that transferring knowledge from other cancers improves the accuracy of network construction. Moreover, TLGP outperforms state-of-the-art approaches in terms of accuracy, improving at least 5%.ConclusionThe proposed model and method provide an effective and efficient strategy for gene ranking by integrating genomic data from various cancers.
Keywords: Gene prioritizatio, Transfer learning, Gene co-expression network, Integrative analysis
College: College of Science
Issue: S9