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Stratified random forest for genome-wide association study
Qingyao Wu
*
, Yunming Ye
, Yang Liu
,
Michael Ng
*
Corresponding author for this work
Department of Mathematics
Research output
:
Chapter in book/report/conference proceeding
›
Conference proceeding
›
peer-review
2
Citations (Scopus)
Overview
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Dive into the research topics of 'Stratified random forest for genome-wide association study'. Together they form a unique fingerprint.
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Keyphrases
Single nucleotide Polymorphism
100%
Random Forest
100%
Genome-wide Association Study
100%
Stratified Random
100%
Case-control Data
50%
High-dimensional Data
33%
Feature Subspace
33%
Stratified Sampling Method
33%
Genome-wide Association
33%
Subspace Selection
16%
Complex Disease
16%
Alzheimer
16%
Simple Random Sampling
16%
Decision Tree
16%
Exhaustive Search
16%
Lower Error Bounds
16%
Genome-wide Single nucleotide Polymorphisms
16%
Generation Method
16%
Accuracy Error
16%
Random Sampling Method
16%
Parkinson
16%
Computer Science
Random Decision Forest
100%
Wide Association Study
100%
Stratified Random
100%
High Dimensional Data
40%
Simple Random Sample
20%
Decision Tree
20%
Complex Disease
20%
Mathematics
Dimensional Data
100%
Stratified Sampling
100%
Error Bound
50%
Decision Tree
50%
Simple Random Sample
50%
Biochemistry, Genetics and Molecular Biology
Single-Nucleotide Polymorphism
100%
Genome-Wide Association Study
100%
Random Forest
100%
Decision Tree
14%
Earth and Planetary Sciences
Random Sampling
100%