M. Jafari

Deep Plasma Proteome Profiling by Modulating Single Nanoparticle Protein Corona with Small Molecules

This paper demonstrates that adding small molecules to plasma enhances nanoparticle protein corona diversity, significantly increasing the number of detectable proteins in plasma proteome profiling.

Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling

This paper identifies effective and low-toxicity drug combinations for AML using a bipartite network model and patient-derived data, finding ruxolitinib-ulixertinib and sapanisertib-LY3009120 as the most synergistic.

Monitoring Functional Post-Translational Modifications Using a Data-Driven Proteome Informatic Pipeline

The paper introduces a PTM-centric proteome informatic pipeline to predict and analyze post-translational modifications in mass spectrometry data, demonstrating its application in glycoproteomics and drug response studies.

Matrix Softening Controls Stretch-Induced Cellular Memory and Fibroblast Activation

This study shows that matrix softening influences stretch-induced cellular memory and fibroblast activation, with a model predicting activation levels based on ECM properties.

The ENDS of assumptions; an online tool for the Epistemic Nonparametric Drug-response Scoring

The paper introduces nonparametric models for drug dose-response curve fitting and provides an online tool for analysis.

Amino acids targeted based metabolomics study in non-segmental Vitiligo: a pilot study

This study uses metabolomics to analyze amino acid profiles in vitiligo patients, identifying biomarkers associated with disease severity and potential pathogenic mechanisms.

A Community Challenge for Pancancer Drug Mechanism of Action Inference from Perturbational Profile Data

The paper hosts a DREAM Challenge to evaluate computational models for predicting drug polypharmacology using perturbational profile data from 32 kinase inhibitors.

Predicting Meridian in Chinese Traditional Medicine Using Machine Learning Approaches

This study uses machine learning to predict TCM Meridians based on molecular features of herb compounds, achieving 83% accuracy.