Triclustering in gene expression data analysis: A selected survey

• Published in: 2011 2nd National Conference on Emerging Trends and Applications in Computer Science pp. 1 - 6
• Main Authors: Mahanta, P, Ahmed, H A, Bhattacharyya, D K, Kalita, J K
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2011
• Subjects: Algorithm design and analysis; Clustering algorithms; Coherence; Data mining; Gene expression; GST data; gTRICLUSTER; Time series analysis; TRICLUSTER; triclustering
• ISBN: 1424495784; 9781424495788
• DOI: 10.1109/NCETACS.2011.5751409

Mining microarray data sets is important in bioinformatics research and biomedical applications. Recently, mining triclusters or 3D clusters in a Gene Sample Time or 3D microarray data is an emerging area of research. Each tricluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. There is a scarcity of triclustering algorithms in the literature of microarray data analysis. We review some existing triclustering algorithms and discuss their merits and demerits. Finally we are trying to provide the researcher who are new to this field a base platform by exposing the issues which are still challenging in triclustering through our analysis of these algorithms.