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A hybrid feature selection method for complex diseases SNPs
Raid Al-Zubi
,
Naeem Ramzan
, Hadeel Alzoubi
, Abbes Amira
Research output
:
Contribution to journal
›
Article
›
peer-review
46
Link opens in a new tab
Citations (Scopus)
165
Downloads (Pure)
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Dive into the research topics of 'A hybrid feature selection method for complex diseases SNPs'. Together they form a unique fingerprint.
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Keyphrases
Single nucleotide Polymorphism
100%
Complex Disease
100%
Hybrid Feature Selection Method
100%
Feature Selection Methods
42%
Conditional mutual Information Maximization
28%
Human Disease
14%
Whole Genome
14%
K-nearest Neighbor (K-NN)
14%
Naïve Bayes
14%
Classification Accuracy
14%
Genomic Data
14%
Human Genome
14%
Linear Discriminant Analysis
14%
Medical Diagnosis
14%
Four States
14%
Support Vector Machine
14%
Art Features
14%
Information Gene
14%
National Center for Biotechnology Information
14%
Gene Expression Omnibus
14%
SVM-RFE
14%
Wrapper Method
14%
Machine Learning Techniques
14%
High Classification Accuracy
14%
High-dimensional Space
14%
Bayes Linear
14%
Maximization Methods
14%
Correlation-based Feature Selection
14%
Fast Correlation
14%
Minimum Redundancy Maximum Relevance (mRMR)
14%
Genome Variability
14%
Computer Science
Feature Selection
100%
Complex Disease
100%
Feature Extraction
50%
Mutual Information
33%
Classification Accuracy
33%
Support Vector Machine
33%
Experimental Result
16%
Linear Discriminant Analysis
16%
Machine Learning Technique
16%
Data Repository
16%
Minimum Redundancy
16%
Medical Diagnosis
16%
High Dimensional Space
16%
Biochemistry, Genetics and Molecular Biology
Single-Nucleotide Polymorphism
100%
Feature Extraction
100%
Support Vector Machine
28%
Gene Expression
14%
Human Genome
14%
Bayesian Learning
14%
K Nearest Neighbor
14%
Genomics
14%
Chemical Engineering
Support Vector Machine
100%
Learning System
50%
Chemistry
Gene Expression
100%