Haiyan Guo, Shujuan Cao, Chen Zhou, Xiaolu Wu, Yongming Zou
{"title":"异构网络中基于模块划分和类重力方法的阿尔茨海默病基本基因预测","authors":"Haiyan Guo, Shujuan Cao, Chen Zhou, Xiaolu Wu, Yongming Zou","doi":"10.37394/232011.2022.17.20","DOIUrl":null,"url":null,"abstract":"The pathogenic mechanism of Alzheimer's disease (AD) is complicated, predicting AD essential genes is an important task in biomedical research, which is helpful in elucidating AD mechanisms and revealing therapeutic targets. In this paper, we propose a random walk algorithm with a restart in the heterogeneous network based on module partition and a gravity-like method (RWRHNMGL) for identifying AD essential genes. The phenotype-gene heterogeneous network (PGHN) is constructed from multiple data sources by considering similar information. These nodes of the optimal module, selected by module partition and covering most functions of AD gene networks, are taken as gene seeds. A refined random walk algorithm is developed to work in the PGHN, the transition matrix is modified by adding a gravity-like method based on subcellular location information, and candidate genes are scored and ranked by a stable probability vector. Finally, the receiver operating characteristic curve (ROC) and Mean Reciprocal Rank is used to evaluate the prediction results of RWRHNMGL. The results show that the RWRHNMGL algorithm performs better in predicting essential genes of AD.","PeriodicalId":53603,"journal":{"name":"WSEAS Transactions on Applied and Theoretical Mechanics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting Essential Genes of Alzheimer Disease based on Module Partition and Gravity-like Method in Heterogeneous Network\",\"authors\":\"Haiyan Guo, Shujuan Cao, Chen Zhou, Xiaolu Wu, Yongming Zou\",\"doi\":\"10.37394/232011.2022.17.20\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The pathogenic mechanism of Alzheimer's disease (AD) is complicated, predicting AD essential genes is an important task in biomedical research, which is helpful in elucidating AD mechanisms and revealing therapeutic targets. In this paper, we propose a random walk algorithm with a restart in the heterogeneous network based on module partition and a gravity-like method (RWRHNMGL) for identifying AD essential genes. The phenotype-gene heterogeneous network (PGHN) is constructed from multiple data sources by considering similar information. These nodes of the optimal module, selected by module partition and covering most functions of AD gene networks, are taken as gene seeds. A refined random walk algorithm is developed to work in the PGHN, the transition matrix is modified by adding a gravity-like method based on subcellular location information, and candidate genes are scored and ranked by a stable probability vector. Finally, the receiver operating characteristic curve (ROC) and Mean Reciprocal Rank is used to evaluate the prediction results of RWRHNMGL. The results show that the RWRHNMGL algorithm performs better in predicting essential genes of AD.\",\"PeriodicalId\":53603,\"journal\":{\"name\":\"WSEAS Transactions on Applied and Theoretical Mechanics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"WSEAS Transactions on Applied and Theoretical Mechanics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37394/232011.2022.17.20\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"WSEAS Transactions on Applied and Theoretical Mechanics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37394/232011.2022.17.20","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
Predicting Essential Genes of Alzheimer Disease based on Module Partition and Gravity-like Method in Heterogeneous Network
The pathogenic mechanism of Alzheimer's disease (AD) is complicated, predicting AD essential genes is an important task in biomedical research, which is helpful in elucidating AD mechanisms and revealing therapeutic targets. In this paper, we propose a random walk algorithm with a restart in the heterogeneous network based on module partition and a gravity-like method (RWRHNMGL) for identifying AD essential genes. The phenotype-gene heterogeneous network (PGHN) is constructed from multiple data sources by considering similar information. These nodes of the optimal module, selected by module partition and covering most functions of AD gene networks, are taken as gene seeds. A refined random walk algorithm is developed to work in the PGHN, the transition matrix is modified by adding a gravity-like method based on subcellular location information, and candidate genes are scored and ranked by a stable probability vector. Finally, the receiver operating characteristic curve (ROC) and Mean Reciprocal Rank is used to evaluate the prediction results of RWRHNMGL. The results show that the RWRHNMGL algorithm performs better in predicting essential genes of AD.
期刊介绍:
WSEAS Transactions on Applied and Theoretical Mechanics publishes original research papers relating to computational and experimental mechanics. We aim to bring important work to a wide international audience and therefore only publish papers of exceptional scientific value that advance our understanding of these particular areas. The research presented must transcend the limits of case studies, while both experimental and theoretical studies are accepted. It is a multi-disciplinary journal and therefore its content mirrors the diverse interests and approaches of scholars involved with fluid-structure interaction, impact and multibody dynamics, nonlinear dynamics, structural dynamics and related areas. We also welcome scholarly contributions from officials with government agencies, international agencies, and non-governmental organizations.