Association·arcadia
Feature selection improves deep learning performance with many uninformative QTLs
Claim that applying feature selection (LASSO approximation) improves MLP model performance when many uninformative QTLs are present.
Confidence
90%
active
Evidence Quote
“Feature selection via a LASSO-like approach improves deep learning performance in the presence of many uninformative QTLs.”
Relationship
Marker feature selection increases Multilayer perceptron (MLP) deep learning model
Connections (7)
Deep learning capture of epistatic variance in genotype-phenotype mappingInferenceChain
Genome contamination yields HGT false positivesAssociation
Data regimes and deep learning benefits in genotype-phenotype mappingInferenceChain
Data regimes and genetic architecture constrain deep learning benefitsInferenceChain
Reasoning on deep learning benchmarking in genotype–phenotype mappingInferenceChain
Deep learning architecture and theoretical foundationsInferenceChain
Reasoning on deep learning and genotype-phenotype predictionInferenceChain
Evidence
“All code, including analysis notebooks, synthetic phenotype generator, and autoencoder model related to the study, made available on Zenodo.”
Author Unknown (2024). When do deep learning models outperform linear regression? doi:10.5281/zenodo.15644565 ↗
“Simulated data and analysis scripts made publicly available on Zenodo for reproducibility of genotype-phenotype mapping results.”
Synthesis for current paper
“Compiled empirical results from various deep learning benchmarking studies showing heterogeneity in model performance across datasets and genetic architectures.”
Synthesis for current paper