Name
Title | ||
|---|---|---|
Neural networks approaches for discovering the learnable correlation between gene function and gene expression in mouse |
Abstract | ||
|---|---|---|
Identifying gene function has many useful applications. Identifying gene function based on gene expression data is much easier in prokaryotes than eukaryotes due to the relatively simple structure of prokaryotes. Recent studies have shown that there is a strong learnable correlation between gene function and gene expression. In previous work, we presented novel clustering and neural network (NN) approaches for predicting mouse gene functions from gene expression. In this paper, we build on that work to significantly improve the clustering distribution and the network prediction error by using a different clustering algorithm along with a new NN training technique. Our results show that NNs can be extremely useful in this area. We present the improved results along with comparisons. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.neucom.2008.04.035 | Neurocomputing |
Keywords | Field | DocType |
neural network,multilayer perceptron,prediction error,neural networks,gene expression | Mean squared prediction error,Gene,Pattern recognition,Gene expression,Correlation,Artificial intelligence,Artificial neural network,Cluster analysis,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
71 | 16-18 | 0925-2312 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emad A. M. Andrews | 1 | 18 | 2.74 |
| Quaid Morris | 2 | 689 | 64.56 |
| Anthony J. Bonner | 3 | 733 | 422.63 |