K. Theofilatos, Christos M. Dimitrakopoulos, Christos E. Alexakos, A. Korfiati, S. Likothanassis, S. Mavroudi
{"title":"InSyBio bioonets:基于网络的生物标志物发现的有效工具","authors":"K. Theofilatos, Christos M. Dimitrakopoulos, Christos E. Alexakos, A. Korfiati, S. Likothanassis, S. Mavroudi","doi":"10.14806/EJ.22.0.871","DOIUrl":null,"url":null,"abstract":"Biological networks have been widely used in systems biology in order to model the complex interactions of molecular players such as proteins, genes, mRNAs, non-coding RNAs and others. However, most of the current methods for biomarker discovery do not use biological networks, but just deploy simple statistical methods to identify differentially expressed genes and gene products. In the present paper, we present InSyBio BioNets, which is a cloud-based web platform offering a unique biomarker discovery pipeline, which combines differential expression analysis and a method for comparing biological networks to identify biomarkers efficiently. As a case study, InSyBio BioNets was applied to a Parkinson dataset of gene expression measurements and outperformed a standard statistical approach by recovering a more compact and informative set of biomarkers.","PeriodicalId":72893,"journal":{"name":"EMBnet.journal","volume":"178 1","pages":"871"},"PeriodicalIF":0.0000,"publicationDate":"2016-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"InSyBio BioNets: an efficient tool for network-based biomarker discovery\",\"authors\":\"K. Theofilatos, Christos M. Dimitrakopoulos, Christos E. Alexakos, A. Korfiati, S. Likothanassis, S. Mavroudi\",\"doi\":\"10.14806/EJ.22.0.871\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Biological networks have been widely used in systems biology in order to model the complex interactions of molecular players such as proteins, genes, mRNAs, non-coding RNAs and others. However, most of the current methods for biomarker discovery do not use biological networks, but just deploy simple statistical methods to identify differentially expressed genes and gene products. In the present paper, we present InSyBio BioNets, which is a cloud-based web platform offering a unique biomarker discovery pipeline, which combines differential expression analysis and a method for comparing biological networks to identify biomarkers efficiently. As a case study, InSyBio BioNets was applied to a Parkinson dataset of gene expression measurements and outperformed a standard statistical approach by recovering a more compact and informative set of biomarkers.\",\"PeriodicalId\":72893,\"journal\":{\"name\":\"EMBnet.journal\",\"volume\":\"178 1\",\"pages\":\"871\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-12-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"EMBnet.journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14806/EJ.22.0.871\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"EMBnet.journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14806/EJ.22.0.871","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
InSyBio BioNets: an efficient tool for network-based biomarker discovery
Biological networks have been widely used in systems biology in order to model the complex interactions of molecular players such as proteins, genes, mRNAs, non-coding RNAs and others. However, most of the current methods for biomarker discovery do not use biological networks, but just deploy simple statistical methods to identify differentially expressed genes and gene products. In the present paper, we present InSyBio BioNets, which is a cloud-based web platform offering a unique biomarker discovery pipeline, which combines differential expression analysis and a method for comparing biological networks to identify biomarkers efficiently. As a case study, InSyBio BioNets was applied to a Parkinson dataset of gene expression measurements and outperformed a standard statistical approach by recovering a more compact and informative set of biomarkers.