Name
Title | ||
|---|---|---|
A Computational Inference Framework for analyzing Gene Regulation Pathway using Microarray Data |
Abstract | ||
|---|---|---|
Microarray experiments produce gene expression data at such a high speed and volume that it is imperative to use highly specialized computational tools for their analyses. One group of such computational tools deals with, namely, "meta-analysis" of microarray data. This step attempts to extract biological interpretations from the identified gene expression pattern. One particular aspect of meta-analysis is sorting out which gene regulation pathways are active and/or inhibited. The focus on this paper is to propose a computational framework with which scientists can compare microarray data with known gene regulation networks that are formed by two known binary gene regulation relationships, activate and inhibit. Using this framework scientists can conduct numerous analysis tasks including (i) identify active or inhibited sub-networks out of massively interconnected gene regulation pathways, (ii) find key genes, namely hubs, that are inferred to be widely involved in multiple aspects of gene regulation, (iii) identify regions of the network that contradict the known regulation, and (iv) estimate the direction of expression of genes that were not included in the microarray experiment. One known utility of this meta-analysis is to help scientists to identify a group of genes that they have missed in earlier experiments and should include in their subsequent experiments. Another utility is to enable them to isolate the group of genes that should be followed more closely using higher accuracy gene expression assays. We introduce three separate but inter-related meta-analysis methodologies, namely, FCFS, Hub majority, and SDP-based. We illustrate our proposed framework using microarray data derived from noggin treated osteoblast cells. The example clearly shows finding sub-networks related to B-catenin which should be expected and thus demonstrates the effectiveness of our proposed framework |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/BIBE.2006.253293 | BIBE |
Keywords | Field | DocType |
cellular biophysics,b-catenin,fcfs,osteoblast cells,inference mechanisms,genetics,scientific information systems,higher accuracy gene expression,key gene,computational inference framework,meta analysis,hub majority,biology computing,biological knowledge extraction,molecular biophysics,microarray data,binary gene regulation,knowledge acquisition,gene expression pattern,gene expression data,sdp,binary gene regulation relationship,gene regulation,microarray experiment,gene regulation pathway,meta data,known gene regulation network,numerical analysis,gene expression | Gene,Microarray,Computer science,Inference,Gene expression,Regulation of gene expression,Sorting,Microarray analysis techniques,Artificial intelligence,Bioinformatics,Machine learning,Gene expression profiling | Conference |
ISBN | Citations | PageRank |
0-7695-2727-2 | 0 | 0.34 |
References | Authors | |
4 | 10 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| D. G. Shin | 1 | 122 | 116.10 |
| John Bluis | 2 | 15 | 2.98 |
| Yoo-ah Kim | 3 | 335 | 22.65 |
| Winfried Krueger | 4 | 3 | 1.46 |
| Jeffrey Maddox | 5 | 0 | 0.68 |
| Ravi Nori | 6 | 2 | 2.47 |
| Nathan Viniconis | 7 | 0 | 0.34 |
| Hsin-Wei Wang | 8 | 42 | 6.56 |
| Alan Wong | 9 | 0 | 0.34 |
| David Rowe | 10 | 0 | 0.34 |