Sarah Mubeen

Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities

This study identifies shared genetic and biological mechanisms between schizophrenia, bipolar disorder, and type 2 diabetes mellitus using integrative analysis of gene expression datasets.

Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets

This work analyzes transcriptomic data across multiple biological contexts to identify unique and shared gene expression patterns using co-expression networks and pathway analysis.

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

RPath prioritizes drugs for diseases by analyzing causal paths in knowledge graphs using drug-perturbed and disease-specific transcriptomic signatures, outperforming existing methods.

DecoPath: A web application for decoding pathway enrichment analysis

DecoPath is a web tool that helps interpret pathway enrichment analysis results by comparing across databases and methods, highlighting consensus and discrepancies.

Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

This paper investigates transcriptomic signatures across diseases by integrating co-expression networks and pathway knowledge to identify disease mechanisms.

CLEP: A Hybrid Data- and Knowledge- Driven Framework for Generating Patient Representations

CLEP generates patient representations by integrating prior biological knowledge with patient data, improving classification performance compared to using raw transcriptomics data.

MultiPaths: a Python framework for analyzing multi-layer biological networks using diffusion algorithms

The paper introduces MultiPaths, a Python framework for analyzing multi-layer biological networks using diffusion algorithms, showing improved predictive performance through integrated multi-omics data.

Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures

This paper uses predictive machine learning models to simulate drug responses by calibrating patient-specific pathway signatures, identifying potential drugs and mechanisms for three cancers.

Drug2ways: Reasoning over causal paths in biological networks for drug discovery

Drug2ways predicts drug candidates by analyzing causal paths in biological networks for disease treatment and drug repositioning.

The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling

This study benchmarks the impact of pathway database choice on enrichment analysis and predictive modeling, showing that using multiple or integrative databases improves consistency and performance.

PathMe: Merging and exploring mechanistic pathway knowledge

PathMe harmonizes three pathway databases into a unified schema to enable comprehensive exploration of pathway crosstalk and consensus.

Integration of Structured Biological Data Sources using Biological Expression Language

The paper introduces Bio2BEL, a set of Python packages that convert multi-modal biological data into Biological Expression Language for integration and analysis.