Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Introduction to Artificial Neural Systems
Introduction to Artificial Neural Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
A quantitative comparison of different MLP activation functions in classification
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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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.