Faculty profile
Alioune Ngom
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Research
Latest papers
A survey of contrastive learning methods in molecular representation.
Briefings in bioinformatics · 2026 · senior author
PolyLLM: polypharmacy side effect prediction via LLM-based SMILES encodings.
Frontiers in pharmacology · 2025 · senior author
Ligand-receptor dynamics in heterophily-aware graph neural networks for enhanced cell type prediction from single-cell RNA-seq data.
Frontiers in molecular biosciences · 2025
Latest funding
- $42,000
Graph Representation Learning Approaches for Computational Drug Repurposing and Bio-Molecular Association Prediction
NSERC · 2024 · Principal investigator
- $32,010
Computing Workstations for Deep Learning on Graph-Structured Data
NSERC · 2022 · Co-investigator
- $132,000
Integrative Network-Based Machine Learning Approaches for Cancer Bioinformatics and Bio-Molecular Network Reconstruction
NSERC · 2016 · Principal investigator
17 publications.
A survey of contrastive learning methods in molecular representation.
Forooghi A, Sadeghi S, Rueda L, Ngom A
PolyLLM: polypharmacy side effect prediction via LLM-based SMILES encodings.
Hakim S, Ngom A
Ligand-receptor dynamics in heterophily-aware graph neural networks for enhanced cell type prediction from single-cell RNA-seq data.
Duan L, Hashemi M, Ngom A, Rueda L
Can large language models understand molecules?
Sadeghi S, Bui A, Forooghi A, Lu J, Ngom A
An Integrative Heterogeneous Graph Neural Network-Based Method for Multi-Labeled Drug Repurposing.
Sadeghi S, Lu J, Ngom A
Computationally repurposing drugs for breast cancer subtypes using a network-based approach.
Firoozbakht F, Rezaeian I, Rueda L, Ngom A
A network-based drug repurposing method via non-negative matrix factorization.
Sadeghi S, Lu J, Ngom A
A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer.
Tabl AA, Alkhateeb A, ElMaraghy W, Rueda L, Ngom A
The predictive performance of short-linear motif features in the prediction of calmodulin-binding proteins.
Li Y, Maleki M, Carruthers NJ, Stemmer PM, Ngom A, Rueda L
A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies.
Tabl AA, Alkhateeb A, Pham HQ, Rueda L, ElMaraghy W, Ngom A
Graph Representation Learning Approaches for Computational Drug Repurposing and Bio-Molecular Association Prediction
Principal investigators: Ngom, Alioune
Keywords: computational systems biology; network-based machine learning; deep/graph representation learning; multi--omics/view data integration; link/interaction/association prediction; graph neural networks; machine learning and pattern recognition; feature selection/extraction; multi-label learning; drug/target/disease prediction
Computing Workstations for Deep Learning on Graph-Structured Data
Principal investigators: Fani, Hossein
Integrative Network-Based Machine Learning Approaches for Cancer Bioinformatics and Bio-Molecular Network Reconstruction
Principal investigators: Ngom, Alioune
High-order/variable-order dynamic Bayesian networks and dynamic qualitative probabilistic networks --- new models of gene regulatory networks
Principal investigators: Ngom, Alioune
Computational intelligent methods for the analysis of molecular data
Principal investigators: Ngom, Alioune
Machine inference research lab
Principal investigators: Goodwin, Scott
Computational approaches for the analysis of molecular data
Principal investigators: Ngom, Alioune
Multiple-valued logic neural networks and DNA sequences corrections
Principal investigators: Ngom, Alioune
From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.
Frequent collaborators
- Luis Rueda and Alioune Ngom: 9 shared papers
- Alioune Ngom and Jianguo Lu: 3 shared papers
- Waguih ElMaraghy and Luis Rueda: 2 shared papers
- Waguih ElMaraghy and Alioune Ngom: 2 shared papers
- Lisa Porter and Luis Rueda: 1 shared paper
- Lisa Porter and Alioune Ngom: 1 shared paper
- Computer Science, School of
- Mechanical, Automotive and Materials Engineering (MAME)
- Biological Sciences
Co-authors at University of Windsor, colored by department. Thicker lines mean more shared papers; select anyone to open their profile and their own map.
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