A Latent Feature Model Approach to Biclustering
نویسندگان
چکیده
Biclustering is the unsupervised learning task of mining a data matrix for useful submatrices, for instance groups of genes that are co-expressed under particular biological conditions. As these submatrices are expected to partly overlap, a significant challenge in biclustering is to develop methods that are able to detect overlapping biclusters. The authors propose a probabilistic mixture modelling framework for biclustering biological data that lends itself to various data types and allows biclusters to overlap. Their framework is akin to the latent feature and mixture-of-experts model families, with inference and parameter estimation being performed via a variational expectation-maximization algorithm. The model compares favorably with competing approaches, both in a binary DNA copy number variation data set and in a miRNA expression data set, indicating that it may potentially be used as a general-problem solving tool in biclustering. KeywoRDS Biclustering, Bioinformatics, Gene Expression, Genomics, Machine Learning
منابع مشابه
Gene co-expression networks via biclustering Differential gene co-expression networks via Bayesian biclustering models
Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-re...
متن کاملDifferential gene co-expression networks via Bayesian biclustering models
Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-re...
متن کاملContext Specific and Differential Gene Co-expression Networks via Bayesian Biclustering
Identifying latent structure in high-dimensional genomic data is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-...
متن کاملAn application of Measurement error evaluation using latent class analysis
Latent class analysis (LCA) is a method of evaluating non sampling errors, especially measurement error in categorical data. Biemer (2011) introduced four latent class modeling approaches: probability model parameterization, log linear model, modified path model, and graphical model using path diagrams. These models are interchangeable. Latent class probability models express l...
متن کاملBiclustering and Feature Selection Techniques in Bioinformatics
The paper describes several data mining techniques, developed to solve problems which are faced by biologists in Bioinformatics.Several biclustering algorithms which perform clustering on the two dimensions simultaneously are described. Other techniques described in this paper include feature selection methods which help in reducing noise and improving the performance of the classification model.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- IJKDB
دوره 6 شماره
صفحات -
تاریخ انتشار 2016