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Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization

BACKGROUND: Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biologic...

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Autores principales: Huang, Jiang, Wu, Min, Lu, Fan, Ou-Yang, Le, Zhu, Zexuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929405/
https://www.ncbi.nlm.nih.gov/pubmed/31870274
http://dx.doi.org/10.1186/s12859-019-3197-3
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author Huang, Jiang
Wu, Min
Lu, Fan
Ou-Yang, Le
Zhu, Zexuan
author_facet Huang, Jiang
Wu, Min
Lu, Fan
Ou-Yang, Le
Zhu, Zexuan
author_sort Huang, Jiang
collection PubMed
description BACKGROUND: Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments. RESULTS: In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction. CONCLUSIONS: In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.
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spelling pubmed-69294052019-12-30 Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization Huang, Jiang Wu, Min Lu, Fan Ou-Yang, Le Zhu, Zexuan BMC Bioinformatics Research BACKGROUND: Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments. RESULTS: In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction. CONCLUSIONS: In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods. BioMed Central 2019-12-24 /pmc/articles/PMC6929405/ /pubmed/31870274 http://dx.doi.org/10.1186/s12859-019-3197-3 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Huang, Jiang
Wu, Min
Lu, Fan
Ou-Yang, Le
Zhu, Zexuan
Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
title Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
title_full Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
title_fullStr Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
title_full_unstemmed Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
title_short Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
title_sort predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929405/
https://www.ncbi.nlm.nih.gov/pubmed/31870274
http://dx.doi.org/10.1186/s12859-019-3197-3
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