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
Machine Learning
Self-Organizing Maps
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
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
Connectionist approaches for predicting mouse gene function from gene expression
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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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.