Name
Abstract | ||
|---|---|---|
Identifying gene function has many useful applications especially in Gene Therapy. Identifying gene function based on gene expression data is much easier in prokaryotes than eukaryotes due to the relatively simple structure of prokaryotes. That is why tissue-specific expression is the primary tool for identifying gene function in eukaryotes. However, recent studies have shown that there is a strong learnable correlation between gene function and gene expression. This paper outlines a new approach for gene function prediction in mouse. The prediction mechanism depends on using Artificial Neural Networks (NN) to predict gene function based on quantitative analysis of gene co-expression. Our results show that neural networks can be extremely useful in this area. Also, we explore clustering of gene functions as a preprocessing step for predicting gene function. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11893028_32 | ICONIP (1) |
Keywords | Field | DocType |
gene expression,neural network,artificial neural network,quantitative analysis | Gene,Computer science,Artificial intelligence,Computational biology,Cluster analysis,Artificial neural network,Connectionism,Step function,Genetic enhancement,Gene expression,Gene prediction,Bioinformatics,Machine learning | Conference |
Volume | ISSN | ISBN |
4232 | 0302-9743 | 3-540-46479-4 |
Citations | PageRank | References |
2 | 0.41 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emad A. M. Andrews Shenouda | 1 | 6 | 1.12 |
| Quaid Morris | 2 | 689 | 64.56 |
| Anthony J. Bonner | 3 | 733 | 422.63 |