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Functional metagenomics: AI-based analysis of complex microbial interactions

Project

Project Details

Program
BioEngineering
Field of Study
Bioinformatics
Division
Biological and Environmental Sciences and Engineering
Faculty Lab Link
https://www.kaust.edu.sa/en/study/faculty/robert-hoehndorf
Center Affiliation
Computational Bioscience Research Center

Project Description

The amount of available protein sequence data is rapidly increasing, for example through applications of sequencing technologies to metagenomics. To understand biological phenomena on a molecular scale, it is crucial to determine the functions of proteins as well as their interactions. Experimental identification of protein functions will not scale to the current and rapidly increasing amount of available protein sequences. Function prediction methods using machine learning may be used to determine protein functions from their sequence. However, proteins rarely function alone but rely on other proteins to perform their function through direct and indirect interactions. The aim of the project is to computationally characterize the functions and interactions of proteins in metagenomes through the development and application of novel AI methods.

About the Researcher

Robert Hoehndorf
Associate Professor, Computer Science
Computer, Electrical and Mathematical Science and Engineering Division

Affiliations

Education Profile

  • Ph.D., University of Leipzig, 2009
  • M.Sc., University of Leipzig, 2005

Research Interests

a€‹Professor Hoehndorf is interested in artificial intelligence, knowledge representation, biomedical informatics, ontology.

Selected Publications

  • Hoehndorf, R., Queralt-Rosinach, N., a€œData science and symbolic AI: Synergies, challenges and opportunitiesa€. In: Data Science.
  • Boudellioua, I., Mahamad Razali, R. B., Kulmanov, M., Hashish, Y., Bajic, V. B., Goncalves- Serra, E., Schoenmakers, N., Gkoutos, G. V., Schofield, P. N., Hoehndorf, R., a€œSemantic prioritization of novel causative genomic variantsa€. In: PLOS Computational Biology 13.4 (Apr. 2017), pp. 1a-21.
  • Hoehndorf, R., Schofield, P. N., Gkoutos, G. V., a€œAnalysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseasesa€. In: Scientific Reports 5 (June 2015), p. 10888.
  • Robert Hoehndorf, Tanya Hiebert, Nigel W. Hardy, Paul N. Schofield, Georgios V. Gkoutos, and Michel Dumontier. ""Mouse model phenotypes provide information about human drug targets"". In: Bioinformatics (Oct. 2013).
  • Robert Hoehndorf, Paul N. Schofield, and Georgios V. Gkoutos. ""PhenomeNET: a whole-phenome approach to disease gene discovery"". In: Nucleic Acids Research 39.18 (July 2011), e119.

Desired Project Deliverables

Month 1: identification of AI methods, characterization of metagenomics dataset, technical presentation Month 2: preparation and preprocessing of metagenomics data (QC, assembly) Month 3: implementation of AI method and data analysis, evaluation Month 4: combination of AI methods: protein functions and interactions between proteins Month 5: evaluation results, quantitative characterization Month 6: write-up

Recommended Student Background

bioinformatics
machine learning
statistics
metagenomics
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