IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information
Overview
Neuro-symbolic methods integrating biomedical networks and ontologies for quantitative analysis of disease phenotypes and variants.
Period: 2021–2023
Funding
- KAUST Competitive Research Grant
— Grant ID:
URF/1/4355-01-01(PI) — USD 239,999
Team
- Robert Hoehndorf — PI (KAUST (Professor of Computer Science))
- Paul N Schofield — CoI (University of Cambridge)
- Georgios V Gkoutos — CoI (University of Birmingham)
- Sarah Alghamdi — PhD (alumnus)
- Azza Althagafi — PhD (alumnus)
- Sumyyah Toonsi — PhD (alumnus)
- Fernando Zhapa-Camacho — PhD (alumnus), MSc (alumnus) (KAUST)
- Xi Peng — MSc (alumnus)
- Zhenwei Tang — MSc (alumnus)
Software
- mOWL — Unified Python library for OWL ontology embedding methods; implements logically faithful and representation-complete optimization. https://github.com/bio-ontology-research-group/mowl
- EL Embeddings — Geometric embeddings for the EL++ description logic that preserve subsumption reasoning; predecessor of GeometrE. https://github.com/bio-ontology-research-group/el-embeddings
Publications acknowledging this project (18)
- (2025) Lattice-based $\mathcalALC$ ontology embeddings with saturation
- (2024) Predicting protein functions using positive-unlabeled ranking with ontology-based priors Supplementary Material
- (2024) Neuro-symbolic AI in Life Sciences
- (2023) DeepGOMeta: Functional Insights into Microbial Communities with Deep Learning-Based Protein Function Prediction
- (2022) Exploring the Use of Ontology Components for Distantly-Supervised Disease and Phenotype Named Entity Recognition
- (2022) mOWL: revision document
- (2021) How much do model organism phenotypes contribute to the computational identification of human disease genes?
- (2020) DeepGOWeb: Fast and accurate protein function prediction on the (Semantic) Web
- (2015) The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients
- (2015) The application of Large Language Models to the phenotype-based prioritization of causative genes in rare disease patients
- (2012) Exploring the Use of Ontology Components for Distantly-Supervised Disease and Phenotype Named Entity Recognition
- (2012) Improving the classification of cardinality phenotypes using collections
- (2012) Linking common human diseases to their phenotypes; development of a resource for human phenomics
- (2012) Komenti: A Semantic Text-mining Framework
- (2012) STARVar: Symptom-based Tool for Automatic Ranking of Variants using evidence from literature and genomes
- … and 3 more.
Topics: Applied Ontology, Neuro-symbolic AI, Rare disease