10th IEEE International Conference on Big Knowledge : proceedings : 10-11 November 2019, Beijing, China. IEEE International Conference on Big Knowledge (10th : 2019 : Beijing, China)最新文献

筛选
英文 中文
Knowledge-Guided Biclustering via Sparse Variational EM Algorithm. 通过稀疏变异 EM 算法实现知识引导的双聚类。
Changgee Chang, Jihwan Oh, Eun Jeong Min, Qi Long
{"title":"Knowledge-Guided Biclustering via Sparse Variational EM Algorithm.","authors":"Changgee Chang, Jihwan Oh, Eun Jeong Min, Qi Long","doi":"10.1109/icbk.2019.00012","DOIUrl":"10.1109/icbk.2019.00012","url":null,"abstract":"<p><p>A biclustering in the analysis of a gene expression data matrix, for example, is defined as a set of biclusters where each bicluster is a group of genes and a group of samples for which the genes are differentially expressed. Although many data mining approaches for biclustering exist in the literature, only few are able to incorporate prior knowledge to the analysis, which can lead to great improvements in terms of accuracy and interpretability, and all are limited in handling discrete data types. We propose a generalized biclustering approach that can be used for integrative analysis of multi-omics data with different data types. Our method is capable of utilizing biological information that can be represented by graph such as functional genomics and functional proteomics and accommodating a combination of continuous and discrete data types. The proposed method builds on a generalized Bayesian factor analysis framework and a variational EM approach is used to obtain parameter estimates, where the latent quantities in the loglikelihood are iteratively imputed by their conditional expectations. The biclusters are retrieved via the sparse estimates of the factor loadings and the conditional expectation of the latent factors. In order to obtain the sparse conditional expectation of the latent factors, a novel sparse variational EM algorithm is used. We demonstrate the superiority of our method over several existing biclustering methods in extensive simulation experiements and in integrative analysis of multi-omics data.</p>","PeriodicalId":93240,"journal":{"name":"10th IEEE International Conference on Big Knowledge : proceedings : 10-11 November 2019, Beijing, China. IEEE International Conference on Big Knowledge (10th : 2019 : Beijing, China)","volume":"2019 ","pages":"25-32"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8291726/pdf/nihms-1588833.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39206760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信