Data integration and ontologies for microbial cell factories

Overview

Ontology-based data integration for synthetic biology and metabolic engineering. Hoehndorf led the data-integration work package.

Period: 2016–2018

Funding

  • KAUST Center Competitive Funding — Grant ID: FCC/1/1976-08-01 (WP-lead) — USD 115,691

Team

  • Vladimir Bajic — PI (Former KAUST CBRC director (retired))
  • Robert Hoehndorf — CoI (KAUST (Professor of Computer Science))
  • Miguel Angel Rodriguez Garcia — Postdoc (King Juan Carlos University (Research Scientist))

Software

Publications acknowledging this project (25)

  • (2020) DeepGOWeb: Fast and accurate protein function prediction on the (Semantic) Web
  • (2020) Semantic similarity and machine learning with biomedical ontologies
  • (2019) Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies
  • (2019) DeepGOPlus: Improved protein function prediction from sequence
  • (2019) Formal axioms in biomedical ontologies improve analysis and interpretation of associated data
  • (2019) PathoPhenoDB: linking human pathogens to their disease phenotypes in support of infectious disease research
  • (2019) Ontology based text mining of gene--phenotype associations: application to candidate gene prediction
  • (2018) Ontology-based prediction of cancer driver genes
  • (2018) Formal axioms in biomedical ontologies improve analysis and interpretation of associated data
  • (2018) Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings
  • (2016) DeepPVP: Phenotype-based prioritization of causative variants using deep learning
  • (2016) Self-normalizing learning on biomedical ontologies
  • (2015) Ontology-based prediction of cancer driver genes
  • (2015) Predicting protein functions from sequence by a neuro-symbolic deep learning model
  • (2012) Combining lexical and context features for automatic ontology extension
  • … and 10 more.

Topics: Applied Ontology, Drug mechanisms, Microbial communities, Neuro-symbolic AI