TY - GEN
T1 - Gene relation discovery by mining similar subsequences in time-series microarray data
AU - Tseng, S.
AU - Chen, Lien Chin
AU - Liu, Jian Jie
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Time-series microarray techniques are newly used to monitor large-scale gene expression profiles for studying biological systems. Previous studies have discovered novel regulatory relations among genes by analyzing time-series microarray data. In this study, we investigate the problem of mining similar subsequences in time-series microarray data so as to discover novel gene relations. A functional relationship among genes often presents itself by locally similar and potentially time-shifted patterns in their expression profiles. Although a number ofstudies have been done on time-series data analysis, they are insufficient in handlingfour important issues for time-series microarray data analysis, namely scaling, offset, shift, and noise. We proposed a novel method to address the four issues simultaneously, which consists of three phase, namely angular transformation, symbolic transformation and suffix-tree-based similar subsequences searching. Through experimental evaluation, it is shown that our method can effectively discover biological relations among genes by identifying the similar subsequences. Moreover, the execution efficiency ofour method is much better than other approaches.
AB - Time-series microarray techniques are newly used to monitor large-scale gene expression profiles for studying biological systems. Previous studies have discovered novel regulatory relations among genes by analyzing time-series microarray data. In this study, we investigate the problem of mining similar subsequences in time-series microarray data so as to discover novel gene relations. A functional relationship among genes often presents itself by locally similar and potentially time-shifted patterns in their expression profiles. Although a number ofstudies have been done on time-series data analysis, they are insufficient in handlingfour important issues for time-series microarray data analysis, namely scaling, offset, shift, and noise. We proposed a novel method to address the four issues simultaneously, which consists of three phase, namely angular transformation, symbolic transformation and suffix-tree-based similar subsequences searching. Through experimental evaluation, it is shown that our method can effectively discover biological relations among genes by identifying the similar subsequences. Moreover, the execution efficiency ofour method is much better than other approaches.
UR - http://www.scopus.com/inward/record.url?scp=79952021421&partnerID=8YFLogxK
U2 - 10.1109/CIBCB.2007.4221211
DO - 10.1109/CIBCB.2007.4221211
M3 - Conference contribution
AN - SCOPUS:79952021421
SN - 1424407109
SN - 9781424407101
T3 - 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007
SP - 106
EP - 112
BT - 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, CIBCB 2007
Y2 - 1 April 2007 through 5 April 2007
ER -