Enriching limited information on rare diseases from heterogeneous networks for drug repositioning

Abstract Background The historical data of rare disease is very scarce in reality, so how to perform drug repositioning for the rare disease is a great challenge. Most existing methods of drug repositioning for the rare disease usually neglect father–son information, so it is extremely difficult to...

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Autores principales:	Hongkui Cao, Liang Zhang, Bo Jin, Shicheng Cheng, Xiaopeng Wei, Chao Che
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Rare diseases Drug repositioning Heterogeneous networks Biased random walk Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/feaec5a8178e4bc9910880304185f0bc
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spelling	oai:doaj.org-article:feaec5a8178e4bc9910880304185f0bc2021-11-21T12:28:51ZEnriching limited information on rare diseases from heterogeneous networks for drug repositioning10.1186/s12911-021-01664-x1472-6947https://doaj.org/article/feaec5a8178e4bc9910880304185f0bc2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12911-021-01664-xhttps://doaj.org/toc/1472-6947Abstract Background The historical data of rare disease is very scarce in reality, so how to perform drug repositioning for the rare disease is a great challenge. Most existing methods of drug repositioning for the rare disease usually neglect father–son information, so it is extremely difficult to predict drugs for the rare disease. Method In this paper, we focus on father–son information mining for the rare disease. We propose GRU-Cooperation-Attention-Network (GCAN) to predict drugs for the rare disease. We construct two heterogeneous networks for information enhancement, one network contains the father-nodes of the rare disease and the other network contains the son-nodes information. To bridge two heterogeneous networks, we set a mapping to connect them. What’s more, we use the biased random walk mechanism to collect the information smoothly from two heterogeneous networks, and employ a cooperation attention mechanism to enhance repositioning ability of the network. Result Comparing with traditional methods, GCAN makes full use of father–son information. The experimental results on real drug data from hospitals show that GCAN outperforms state-of-the-art machine learning methods for drug repositioning. Conclusion The performance of GCAN for drug repositioning is mainly limited by the insufficient scale and poor quality of the data. In future research work, we will focus on how to utilize more data such as drug molecule information and protein molecule information for the drug repositioning of the rare disease.Hongkui CaoLiang ZhangBo JinShicheng ChengXiaopeng WeiChao CheBMCarticleRare diseasesDrug repositioningHeterogeneous networksBiased random walkComputer applications to medicine. Medical informaticsR858-859.7ENBMC Medical Informatics and Decision Making, Vol 21, Iss S9, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Rare diseases Drug repositioning Heterogeneous networks Biased random walk Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Rare diseases Drug repositioning Heterogeneous networks Biased random walk Computer applications to medicine. Medical informatics R858-859.7 Hongkui Cao Liang Zhang Bo Jin Shicheng Cheng Xiaopeng Wei Chao Che Enriching limited information on rare diseases from heterogeneous networks for drug repositioning
description	Abstract Background The historical data of rare disease is very scarce in reality, so how to perform drug repositioning for the rare disease is a great challenge. Most existing methods of drug repositioning for the rare disease usually neglect father–son information, so it is extremely difficult to predict drugs for the rare disease. Method In this paper, we focus on father–son information mining for the rare disease. We propose GRU-Cooperation-Attention-Network (GCAN) to predict drugs for the rare disease. We construct two heterogeneous networks for information enhancement, one network contains the father-nodes of the rare disease and the other network contains the son-nodes information. To bridge two heterogeneous networks, we set a mapping to connect them. What’s more, we use the biased random walk mechanism to collect the information smoothly from two heterogeneous networks, and employ a cooperation attention mechanism to enhance repositioning ability of the network. Result Comparing with traditional methods, GCAN makes full use of father–son information. The experimental results on real drug data from hospitals show that GCAN outperforms state-of-the-art machine learning methods for drug repositioning. Conclusion The performance of GCAN for drug repositioning is mainly limited by the insufficient scale and poor quality of the data. In future research work, we will focus on how to utilize more data such as drug molecule information and protein molecule information for the drug repositioning of the rare disease.
format	article
author	Hongkui Cao Liang Zhang Bo Jin Shicheng Cheng Xiaopeng Wei Chao Che
author_facet	Hongkui Cao Liang Zhang Bo Jin Shicheng Cheng Xiaopeng Wei Chao Che
author_sort	Hongkui Cao
title	Enriching limited information on rare diseases from heterogeneous networks for drug repositioning
title_short	Enriching limited information on rare diseases from heterogeneous networks for drug repositioning
title_full	Enriching limited information on rare diseases from heterogeneous networks for drug repositioning
title_fullStr	Enriching limited information on rare diseases from heterogeneous networks for drug repositioning
title_full_unstemmed	Enriching limited information on rare diseases from heterogeneous networks for drug repositioning
title_sort	enriching limited information on rare diseases from heterogeneous networks for drug repositioning
publisher	BMC
publishDate	2021
url	https://doaj.org/article/feaec5a8178e4bc9910880304185f0bc
work_keys_str_mv	AT hongkuicao enrichinglimitedinformationonrarediseasesfromheterogeneousnetworksfordrugrepositioning AT liangzhang enrichinglimitedinformationonrarediseasesfromheterogeneousnetworksfordrugrepositioning AT bojin enrichinglimitedinformationonrarediseasesfromheterogeneousnetworksfordrugrepositioning AT shichengcheng enrichinglimitedinformationonrarediseasesfromheterogeneousnetworksfordrugrepositioning AT xiaopengwei enrichinglimitedinformationonrarediseasesfromheterogeneousnetworksfordrugrepositioning AT chaoche enrichinglimitedinformationonrarediseasesfromheterogeneousnetworksfordrugrepositioning
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Enriching limited information on rare diseases from heterogeneous networks for drug repositioning

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