Shandar Ahmad

This study evaluates cross-cohort generalization of mammography diagnostic models using different image representations and domain-adversarial learning, finding that MedSigLIP embeddings provide the best transferability.

MPGEM harmonizes and completes gene-expression profiles from legacy microarray datasets, enabling large-scale transcriptomic analysis.

This study identifies miRNA-lncRNA interactions and TFs in human labour using machine learning, revealing regulatory networks and a public database for analysis.

This study compares direct pathway enrichment prediction from histopathological WSIs with gene expression-mediated models in TCGA-BRCA, finding direct prediction performs better.

The paper benchmarks five image foundation models for predicting gene expression from whole-slide images using the TCGA-BRCA cohort, finding domain-aligned models outperform general-purpose ones.

This paper generates TLR5-activating molecules using a systems-level approach combining transcriptome data and computational methods, validated experimentally.

The paper develops a pan-cancer, pan-treatment model to predict drug responses using patient-derived xenografts, showing it works well across cancer types with limited training data.

This paper introduces a reduced-memory MLP framework that uses distribution-based weights and genetic algorithm-trained hyperparameters to improve efficiency and performance.

This study uses Hi-C to compare genome organization between virulent and avirulent Mycobacterium tuberculosis strains, finding differences in Chromosome Interacting Domain (CID) size and gene distribution.