SYMMETRIC AND ASYMMETRIC MULTI-MODALITY BICLUSTERING ANALYSIS FOR MICROARRAY DATA MATRIX.

@article{citeulike:741324, abstract = {Machine learning techniques offer a viable approach to cluster discovery from microarray data, which involves identifying and classifying biologically relevant groups in genes and conditions. It has been recognized that genes (whether or not they belong to the same gene group) may be co-expressed via a variety of pathways. Therefore, they can be adequately described by a diversity of coherence models. In fact, it is known that a gene may participate in multiple pathways that may or may not be co-active under all conditions. It is therefore biologically meaningful to simultaneously divide genes into functional groups and conditions into co-active categories - leading to the so-called biclustering analysis. For this, we have proposed a comprehensive set of coherence models to cope with various plausible regulation processes. Furthermore, a multivariate biclustering analysis based on fusion of different coherence models appears to be promising because the expression level of genes from the same group may follow more than one coherence models. The simulation studies further confirm that the proposed framework enjoys the advantage of high prediction performance.}, address = {Department of Electrical Engineering, Princeton University, Engineering Quad, Princeton, New Jersey 08544, USA. kung@princeton.edu.}, author = {Kung, Sun-Yuan Y. and Mak, Man-Wai W. and Tagkopoulos, Ilias }, citeulike-article-id = {741324}, issn = {0219-7200}, journal = {J Bioinform Comput Biol}, keywords = {microarray}, month = {April}, number = {2}, pages = {275--298}, posted-at = {2006-07-06 00:17:45}, priority = {2}, title = {SYMMETRIC AND ASYMMETRIC MULTI-MODALITY BICLUSTERING ANALYSIS FOR MICROARRAY DATA MATRIX.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/16819784}, volume = {4}, year = {2006} }

TY - JOUR ID - citeulike:741324 L3 - citeulike-article-id:741324 TI - SYMMETRIC AND ASYMMETRIC MULTI-MODALITY BICLUSTERING ANALYSIS FOR MICROARRAY DATA MATRIX. JF - J Bioinform Comput Biol VL - 4 IS - 2 SP - 275 EP - 298 AD - Department of Electrical Engineering, Princeton University, Engineering Quad, Princeton, New Jersey 08544, USA. kung@princeton.edu. SN - 0219-7200 N2 - Machine learning techniques offer a viable approach to cluster discovery from microarray data, which involves identifying and classifying biologically relevant groups in genes and conditions. It has been recognized that genes (whether or not they belong to the same gene group) may be co-expressed via a variety of pathways. Therefore, they can be adequately described by a diversity of coherence models. In fact, it is known that a gene may participate in multiple pathways that may or may not be co-active under all conditions. It is therefore biologically meaningful to simultaneously divide genes into functional groups and conditions into co-active categories - leading to the so-called biclustering analysis. For this, we have proposed a comprehensive set of coherence models to cope with various plausible regulation processes. Furthermore, a multivariate biclustering analysis based on fusion of different coherence models appears to be promising because the expression level of genes from the same group may follow more than one coherence models. The simulation studies further confirm that the proposed framework enjoys the advantage of high prediction performance. KW - microarray AU - Kung, Sun-Yuan AU - Mak, Man-Wai AU - Tagkopoulos, Ilias PY - 2006/04// UR - http://view.ncbi.nlm.nih.gov/pubmed/16819784 ER -