Abstract
Motivation: Classifying genes into clusters depending on their expression profiles is one of the most important analysis techniques for microarray data. Because temporal gene expression profiles are indicative of the dynamic functional properties of genes, the application of clustering analysis to time-course data allows the more precise division of genes into functional classes. Conventional clustering methods treat the sampling data at each time point as data obtained under different experimental conditions without considering the continuity of time-course data between time periods t and t + 1. Here, we propose a method designated mathematical model-based clustering (MMBC). Results: The proposed method, designated MMBC, was applied to artificial data and time-course data obtained using Saccharomyces cerevisiae. Our method is able to divide data into clusters more accurately and coherently than conventional clustering methods. Furthermore, MMBC is more tolerant to noise than conventional clustering methods.
| Original language | English |
|---|---|
| Pages (from-to) | 843-848 |
| Number of pages | 6 |
| Journal | Bioinformatics |
| Volume | 22 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - Apr 1 2006 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics
Cite this
Novel technique for preprocessing high dimensional time-course data from DNA microarray : Mathematical model-based clustering. / Hakamada, Kazumi; Okamoto, Masahiro; Hanai, Taizo.
In: Bioinformatics, Vol. 22, No. 7, 01.04.2006, p. 843-848.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Novel technique for preprocessing high dimensional time-course data from DNA microarray
T2 - Mathematical model-based clustering
AU - Hakamada, Kazumi
AU - Okamoto, Masahiro
AU - Hanai, Taizo
PY - 2006/4/1
Y1 - 2006/4/1
N2 - Motivation: Classifying genes into clusters depending on their expression profiles is one of the most important analysis techniques for microarray data. Because temporal gene expression profiles are indicative of the dynamic functional properties of genes, the application of clustering analysis to time-course data allows the more precise division of genes into functional classes. Conventional clustering methods treat the sampling data at each time point as data obtained under different experimental conditions without considering the continuity of time-course data between time periods t and t + 1. Here, we propose a method designated mathematical model-based clustering (MMBC). Results: The proposed method, designated MMBC, was applied to artificial data and time-course data obtained using Saccharomyces cerevisiae. Our method is able to divide data into clusters more accurately and coherently than conventional clustering methods. Furthermore, MMBC is more tolerant to noise than conventional clustering methods.
AB - Motivation: Classifying genes into clusters depending on their expression profiles is one of the most important analysis techniques for microarray data. Because temporal gene expression profiles are indicative of the dynamic functional properties of genes, the application of clustering analysis to time-course data allows the more precise division of genes into functional classes. Conventional clustering methods treat the sampling data at each time point as data obtained under different experimental conditions without considering the continuity of time-course data between time periods t and t + 1. Here, we propose a method designated mathematical model-based clustering (MMBC). Results: The proposed method, designated MMBC, was applied to artificial data and time-course data obtained using Saccharomyces cerevisiae. Our method is able to divide data into clusters more accurately and coherently than conventional clustering methods. Furthermore, MMBC is more tolerant to noise than conventional clustering methods.
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U2 - 10.1093/bioinformatics/btl016
DO - 10.1093/bioinformatics/btl016
M3 - Article
C2 - 16434440
AN - SCOPUS:33645305965
VL - 22
SP - 843
EP - 848
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 7
ER -