TY - GEN
T1 - Gene ontology semi-supervised possibilistic clustering of gene expression data
AU - Maraziotis, Ioannis A.
AU - Dimitrakopoulos, George
AU - Bezerianos, Anastasios
PY - 2012/6/5
Y1 - 2012/6/5
N2 - Clustering is one of the most important data analysis methods with applications of significant importance in many scientific fields. In computational biology, clustering of gene expression data from microarrays assists biologists to investigate uncharacterized genes by identifying biologically relevant groups of genes. Semi-supervised clustering algorithms have proven to bring substantial improvements in the results of standard clustering methods especially on datasets of increased complexity. In this paper we propose a semi-supervised possibilistic clustering algorithm (SSPCA) utilizing supervision via pair-wise constraints indicating whether a pair of patterns should belong to the same cluster or not. Furthermore we show how external sources of biological information like gene ontology data can provide constraints to guide the clustering process of SSPCA. Our results show that the proposed algorithm outperformed other well established standard and semi-supervised methodologies.
AB - Clustering is one of the most important data analysis methods with applications of significant importance in many scientific fields. In computational biology, clustering of gene expression data from microarrays assists biologists to investigate uncharacterized genes by identifying biologically relevant groups of genes. Semi-supervised clustering algorithms have proven to bring substantial improvements in the results of standard clustering methods especially on datasets of increased complexity. In this paper we propose a semi-supervised possibilistic clustering algorithm (SSPCA) utilizing supervision via pair-wise constraints indicating whether a pair of patterns should belong to the same cluster or not. Furthermore we show how external sources of biological information like gene ontology data can provide constraints to guide the clustering process of SSPCA. Our results show that the proposed algorithm outperformed other well established standard and semi-supervised methodologies.
KW - constraints
KW - gene expression
KW - gene ontology
KW - possibilistic clustering
KW - semi-supervision
UR - http://www.scopus.com/inward/record.url?scp=84861662326&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84861662326&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-30448-4_33
DO - 10.1007/978-3-642-30448-4_33
M3 - Conference contribution
AN - SCOPUS:84861662326
SN - 9783642304477
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 262
EP - 269
BT - Artificial Intelligence
T2 - 7th Hellenic Conference on Artificial Intelligence, SETN 2012
Y2 - 28 May 2012 through 31 May 2012
ER -