Sach Mukherjee

Swarm Learning as a privacy-preserving machine learning approach for disease classification

This paper introduces Swarm Learning, a decentralized machine learning approach for disease classification using transcriptomic data, demonstrating its effectiveness in developing classifiers for COVID-19, tuberculosis, and leukemias while preserving privacy.

Diagnostic value of blood gene expression-based classifiers as exemplified for acute myeloid leukemia

The paper develops robust and accurate classifiers for AML diagnosis using blood gene expression data and machine learning across 12,029 samples from 105 studies.

High-dimensional regression over disease subgroups

This paper develops a high-dimensional regression method for disease subgroups, showing improved prediction and subgroup-specific sparsity estimation using Alzheimer's, ALS, and cancer data.

Context-specificity in causal signaling networks revealed by phosphoprotein profiling

The paper investigates context-specificity in signaling networks using phosphoprotein profiling in breast cancer cell lines under various stimuli and kinase inhibition.