{"title":"Visualization of high throughput biological data","authors":"Leishi Zhang, J. Kuljis, Xiaohui Liu","doi":"10.2498/iti.2012.0380","DOIUrl":null,"url":null,"abstract":"The rapid advances in high throughput biotechnology pose great challenges to the data analysis and visualization community. The sheer volume of data and complex biological problems that need to be answered increase the demand for effective data analysis and visualization tools which provide intuitive visual representation and allow full exploitation of the data. In this paper, we examine various visualization techniques that have been applied to high throughput biological data analysis. Several key problem areas as well as possible solutions are explored, and some challenging open issues are highlighted.","PeriodicalId":135105,"journal":{"name":"Proceedings of the ITI 2012 34th International Conference on Information Technology Interfaces","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ITI 2012 34th International Conference on Information Technology Interfaces","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2498/iti.2012.0380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The rapid advances in high throughput biotechnology pose great challenges to the data analysis and visualization community. The sheer volume of data and complex biological problems that need to be answered increase the demand for effective data analysis and visualization tools which provide intuitive visual representation and allow full exploitation of the data. In this paper, we examine various visualization techniques that have been applied to high throughput biological data analysis. Several key problem areas as well as possible solutions are explored, and some challenging open issues are highlighted.