Abstract
Mining large data & deriving meaning from the mined data in Bioinformatics is a computationally intensive & relevant issue. In this paper we present an efficient algorithm to cluster genes into similar 'functional' groups. This is a technique for extracting and characterizing rhythmic expression profiles from genome-wide DNA micro-array hybridization data. These patterns are clues to discovering rhythmic genes implicated in cell-cycle, circadian, or other biological processes. These functionalities are defined in the paper (anti-correlated, similar time expression etc). We present a signal-processing approach to this problem. We also explore an information theoretic criterion for identifying those genes exhibiting maximum variation in behavior. The genes are clustered and then relationships are derived for the proposition of a temporal cell-cycle model governing regulatory behavior. We are presently considering the Human Fibroblast and Yeast data set for analysis.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 316-319 |
| Number of pages | 4 |
| Journal | Conference Record of the Asilomar Conference on Signals, Systems and Computers |
| Volume | 1 |
| State | Published - 2002 |
| Event | The Thirty-Sixth Asilomar Conference on Signals Systems and Computers - Pacific Groove, CA, United States Duration: Nov 3 2002 → Nov 6 2002 |
Keywords
- Entropy
- Micro-array data
- Wavelets
ASJC Scopus subject areas
- Signal Processing
- Computer Networks and Communications