The γ-OMP Algorithm for Feature Selection With Application to Gene Expression Data

The γ-OMP Algorithm for Feature Selection With Application to Gene Expression Data

Feature selection for predictive analytics is the problem of identifying a minimal-size subset of features that is maximally predictive of an outcome of interest. To apply to molecular data, feature selection algorithms need to be scalable to tens of thousands of features. In this paper, we propose

Bibliographic Details
Main Authors: Lakiotaki, K. (Author), Papadovasilakis, Z. (Author), Tsagris, M. (Author), Tsamardinos, I. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
IEEE.04 >
rithm04 > A
ithms04 > bioin
atics04 > Bioin
atics04
ology04 > case cont
study04 > Case-Contro
udies04 > Classification (of inf
tion)04 > Clustering a
ithms04 > Computationa
ology04 > Dat
ature04 > Feature e
ction04 > Feature
ction04 > Feature selection
rithm04 > Features
ction04 > gene e
ssion04 > Gene e
ssion04 > Gene E
ssion04 > gene expres
data04 > Gene Expres
Data04 > Gener
ation04 > high dimensi
data04 > High dimensi
data04 > Logist
odels04 > Molec
data04 >
data04
mics'04 > o
data04 > Predictive
ytics04 > Regression
lysis04 > statisti
model04 > Time
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