Sheikh Muhammad Saiful Islam

Sum-h2, enabling genetic discovery for deep learning-derived phenotypes through a fast evaluation framework and arena of performance

The paper introduces sum-h2, a fast framework for evaluating genetic discovery from deep learning-derived phenotypes using heritability measures, and establishes a Genetic Discovery Arena for benchmarking.

Replicability of unsupervised deep learning derived image phenotypes

This study evaluates the replicability of unsupervised deep learning image phenotypes across different models, data splits, and cohorts, finding significant stability and biological relevance.

Learning dynamics of unsupervised deep learning reveal epoch-specific genetic architectures of brain morphology

The paper uses deep learning dynamics to uncover epoch-specific genetic architectures of brain morphology from UK Biobank data.

Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes

The paper introduces JAGWAS, a multi-phenotype GWAS tool that identifies more genetic loci for brain imaging phenotypes than single-phenotype GWAS.

New phenotype discovery method by unsupervised deep representation learning empowers genetic association studies of brain imaging

This paper introduces an unsupervised deep learning method to derive brain imaging phenotypes from MRI data, identifying new genetic loci associated with these endophenotypes.

Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and Mortality

The paper identifies gene expression patterns in ovarian cancer associated with disease progression and mortality using clinical and transcriptomic data from TCGA.