Raghvendra Mall

MELD predicts SNV functional effects by directly training on wild-type versus mutant differences, outperforming post-hoc in-silico mutagenesis methods with fewer parameters.

This study identifies immune subtypes of colorectal cancer using methylation patterns, linking them to distinct clinical outcomes and immune programs.

This study evaluates CNN and ViT models for predicting gene expression from spatial transcriptomics images, finding that compact CNNs with larger patches perform better for biologically relevant genes.

This study benchmarks protein language models for predicting protein crystallization propensity and identifies novel potentially crystallizable proteins.

MATE-Pred predicts TCR-epitope binding affinity using multi-modal attention, outperforming baselines with +8.4% MCC and +5.5% AUC.

The paper develops machine learning classifiers to predict HIV-1 resistance to 33 neutralizing antibodies with 96% accuracy using clinical isolates.

SolXplain is a sequence-based protein solubility predictor that achieves high accuracy and provides feature-based explanations for its predictions.

RGBM identifies transcriptional regulators of glioma subtypes by inferring gene regulatory networks from diverse data sources and outperforming existing methods on benchmark datasets.

The paper identifies cancer gene biomarkers associated with drug sensitivity and provides open-source tools for analysis.

This paper introduces DCD, a method to detect differential sub-networks in paired biological networks, showing it outperforms existing methods in simulated and real cancer datasets.

The paper applies high-dimensional statistical and network visualization techniques to Arab diabetes and obesity data to identify key biomarkers and interactions associated with the diseases.

The paper introduces an improved method for detecting statistically significant network changes in biological networks, showing better performance on dense random geometric networks and application to glioma cancer regulatory networks.