DNA Microarray Data Analysis: A Novel Biclustering Algorithm Approach
نویسندگان
چکیده
Biclustering algorithms refer to a distinct class of clustering algorithms that perform simultaneous row-column clustering. Biclustering problems arise in DNAmicroarray data analysis, collaborative filtering, market research, information retrieval, text mining, electoral trends, exchange analysis, and so forth. When dealing with DNA microarray experimental data for example, the goal of biclustering algorithms is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this study, we develop novel biclustering algorithms using basic linear algebra and arithmetic tools. The proposed biclustering algorithms can be used to search for all biclusters with constant values, biclusters with constant values on rows, biclusters with constant values on columns, and biclusters with coherent values from a set of data in a timely manner and without solving any optimization problem. We also show how one of the proposed biclustering algorithms can be adapted to identify biclusters with coherent evolution. The algorithms developed in this study discover all valid biclusters of each type, while almost all previous biclustering approaches will miss some.
منابع مشابه
Propagation-Based Biclustering Algorithm for Extracting Inclusion-Maximal Motifs
Biclustering, which is simultaneous clustering of columns and rows in data matrix, became an issue when classical clustering algorithms proved not to be good enough to detect similar expressions of genes under subset of conditions. Biclustering algorithms may be also applied to different datasets, such as medical, economical, social networks etc. In this article we explain the concept beneath h...
متن کاملBiclustering of Gene Expression Using Glowworm Swarm Optimization and Neuro-Fuzzy Discriminant Analysis
-The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Biclustering is the most popular approach of analyzing gene expression data and has indeed proven to be successful in many applications. In recent years, several biclustering methods have been suggested to identify local patterns in gene expression data. Most of these algorithms represent gr...
متن کاملBiclustering with Background Knowledge using Formal Concept Analysis
Biclustering methods have proven to be critical tools in the exploratory analysis of high-dimensional data including information networks, microarray experiments, and bag of words data. However, most biclustering methods fail to answer specific questions of interest and do not incorporate background knowledge and expertise from the user. To this end, query-based biclustering algorithms have bee...
متن کاملQuery-based Biclustering using Formal Concept Analysis
Biclustering methods have proven to be critical tools in the exploratory analysis of high-dimensional data including information networks, microarray experiments, and bag of words data. However, most biclustering methods fail to answer specific questions of interest and do not incorporate prior knowledge and expertise from the user. To this end, query-based biclustering algorithms that are rece...
متن کاملBiBinConvmean : A Novel Biclustering Algorithm for Binary Microarray Data
In this paper, we present a new algorithm called, BiBinConvmean, for biclustering of binary microarray data. It is a novel alternative to extract biclusters from sparse binary datasets. Our algorithm is based on Iterative Row and Column Clustering Combination (IRCCC) and Divide and Conquer (DC) approaches, K-means initialization and the CroBin evaluation function [6]. Applied on binary syntheti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- EURASIP J. Adv. Sig. Proc.
دوره 2006 شماره
صفحات -
تاریخ انتشار 2006