| DOI | Resolve DOI: https://doi.org/10.1080/00207160701203419 |
|---|
| Author | Search for: Phan, Sieu1; Search for: Famili, Fazel1; Search for: Tang, Zuojian1; Search for: Pan, Youlian1; Search for: Liu, Ziying1; Search for: Ouyang, Junjun1; Search for: Lenferink, Anne2; Search for: Mc-Court O'Connor, Maureen2 |
|---|
| Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
- National Research Council of Canada. NRC Biotechnology Research Institute
|
|---|
| Format | Text, Article |
|---|
| Subject | biological pattern recognition; time-series microarray data; data mining; clustering; co-expressed genes |
|---|
| Abstract | Identification of co-expressed genes sharing similar biological behaviours is an essential step in functional genomics. Traditional clustering techniques are generally based on overall similarity of expression levels and often generate clusters with mixed profile patterns. A novel pattern recognition method for selecting co-expressed genes based on rate of change and modulation status of gene expression at each time interval is proposed in this paper. This method is capable of identifying gene clusters consisting of highly similar shapes of expression profiles and modulation patterns. Furthermore, we develop a quality index based on the semantic similarity in gene annotations to assess the likelihood of a cluster being a co-regulated group. The effectiveness of the proposed methodology is demonstrated by applying it to the well-known yeast sporulation dataset and an in-house cancer genomics dataset. |
|---|
| Publication date | 2007-07-02 |
|---|
| In | |
|---|
| Language | English |
|---|
| NRC number | NRCC 48820 |
|---|
| NPARC number | 3539513 |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | ee5b248a-0399-4752-bf71-df4fa4b6914a |
|---|
| Record created | 2009-03-01 |
|---|
| Record modified | 2020-05-10 |
|---|
