Browsing by Subject "Gene expression analysis"

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Open Access
Bi-k-bi clustering: mining large scale gene expression data using two-level biclustering
(Inderscience Enterprises Ltd., 2010) Çarkacioǧlu, L.; Atalay, R.; Konu, O.; Atalay, V.; Can, T.
Due to the increase in gene expression data sets in recent years, various data mining techniques have been proposed for mining gene expression profiles. However, most of these methods target single gene expression data sets and cannot handle all the available gene expression data in public databases in reasonable amount of time and space. In this paper, we propose a novel framework, bi-k-bi clustering, for finding association rules of gene pairs that can easily operate on large scale and multiple heterogeneous data sets. We applied our proposed framework on the available NCBI GEO Homo sapiens data sets. Our results show consistency and relatedness with the available literature and also provides novel associations. Copyright © 2010 Inderscience Enterprises Ltd.
Open Access
Pathway activity inference using microarray data
(Bilkent Center for Bioinformatics (BCBI), 2004) Babur, Özgün; Demir, Emek; Ayaz, Aslı; Doğrusöz, Uğur; Sakarya, Onur
Motivation: Microarray technology provides cell-scale expression data; however, analyzing this data is notoriously difficult. It is becoming clear that system-oriented methods are needed in order to best interpret this data. Combining microarray expression data with previously built pathway models may provide useful insight about the cellular machinery and reveal mechanisms that govern diseases. Given a qualitative state - transition model of the cellular network and an expression profile of RNA molecules, we would like to infer possible differential activity of the other molecules such as proteins on this network. Results: In this paper an efficient algorithm using a new approach is proposed to attack this problem. Using the regulation relations on the network, we determine possible scenarios that might lead to the expression profile, and qualitatively infer the activity differences of the molecules between test and control samples. Availability: This new analysis method has been implemented as part of a microarray data analysis component within PATIKA (Pathway Analysis Tool for Integration and Knowledge Acquisition), which is a software environment for pathway storage, integration and analysis. Facilities for easy analysis and visualization of the results is also provided. Contact: http://www.patika.org.