Paper Info
Title
A Multivariate Algorithm for Gene Selection Based on the Nearest Neighbor Probability
Abstract
Experiments performed with DNA microarrays have very often the aim of retrieving a subset of genes involved in the discrimination between two physiological or pathological states (e.g. ill/healthy). Many methods have been proposed to solve this problem, among which the Signal to Noise ratio (S2N ) [5] and SVM-RFE [6]. Recently, the complementary approach to RFE, called Recursive Feature Addition (RFA ), has been successfully adopted. According to this approach, at each iteration the gene which maximizes a proper ranking function *** is selected, thus producing an ordering among the considered genes. In this paper an RFA method based on the nearest neighbor probability, named NN-RFA , is described and tested on some real world problems regarding the classification of human tissues. The results of such simulations show the ability of NN-RFA in retrieving a correct subset of genes for the problems at hand.
Year
DOI
Venue
2008
10.1007/978-3-642-02504-4_11
CIBB
Keywords
Field
DocType
gene selection,multivariate algorithm,complementary approach,noise ratio,proper ranking function,dna microarrays,human tissue,nearest neighbor probability,pathological state,rfa method,recursive feature addition,correct subset,signal to noise ratio,nearest neighbor,dna microarray
Boolean function,Feature selection,Computer science,Artificial intelligence,Recursion,k-nearest neighbors algorithm,Ranking,Pattern recognition,Multivariate statistics,Signal-to-noise ratio,Algorithm,Bioinformatics,Machine learning,DNA microarray
Conference
Volume
ISSN
Citations 
5488
0302-9743
0
PageRank 
References 
Authors
0.34
5
2
Name
Order
Citations
PageRank
Enrico Ferrari1163.55
Marco Muselli222024.97