Abstract: The availability of massive amounts of experimental data based on genome-wide studies has given impetus in recent years to a large effort in developing mathematical, statistical and computational techniques to infer biological models from data. In many bioinformatics problems the number of features is significantly larger than the number of samples (high feature to sample ratio datasets) and feature selection techniques have become an apparent need in many bioinformatics applications. This article provides the reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, discussing its uses, common and upcoming bioinformatics applications.

S Nirmala Devi and Dr. S.P Rajagopalan, “A study on Feature Selection Techniques in Bio-Informatics” International Journal of Advanced Computer Science and Applications(IJACSA), 2(1), 2011. http://dx.doi.org/10.14569/IJACSA.2011.020121

@article{Devi2011,
title = {A study on Feature Selection Techniques in Bio-Informatics},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2011.020121},
url = {http://dx.doi.org/10.14569/IJACSA.2011.020121},
year = {2011},
publisher = {The Science and Information Organization},
volume = {2},
number = {1},
author = {S Nirmala Devi and Dr. S.P Rajagopalan}
}