Machine learning: neural networks, genetic algorithms, and fuzzy systems
Machine learning: neural networks, genetic algorithms, and fuzzy systems
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Principles of data mining
Bioinformatics—an introduction for computer scientists
ACM Computing Surveys (CSUR)
Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems
Guest Editors' Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 1
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Grammatical Inference in Bioinformatics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Computational Genomics: A Case Studies Approach
Introduction to Computational Genomics: A Case Studies Approach
Classification process analysis of bioinformatics data with a support vector fuzzy inference system
NN'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Neural Networks - Volume 8
Neural networks for prediction of nucleotide sequences by using genomic signals
WSEAS TRANSACTIONS on SYSTEMS
Empirical determination of sample sizes for multi-layer perceptrons by simple RBF networks
WSEAS Transactions on Computers
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
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Machine learning is the adaptive process that makes computers improve from experience, by example, and by analogy. Learning capabilities are essential for automatically enhancing the performance of a computational system over time on the basis of previous history. Bioinformatics is the interdisciplinary science of interpreting biological data using information technology and computer science. The field of bioinformatics main objective is to develop relevant computational systems for biological purposes. In this paper, we study how machine learning can help in developing better bioinformatics methods and tools in a coherent manner. We attempt to integrate the multitude of existing methods and tools in a unifying framework as a prelude to showing how machine learning can uncover even more useful structures hidden in biological sequences.