Functional metagenomics: AI-based analysis of complex microbial interactions

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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.
Program - BioEngineering
Division - Biological and Environmental Sciences and Engineering
Center Affiliation - Computational Bioscience Research Center
Field of Study - Bioinformatics

About the
Researcher

Robert Hoehndorf

Associate Professor, Computer Science

Robert Hoehndorf
‚ÄčProfessor Hoehndorf is interested in artificial intelligence, knowledge representation, biomedical informatics, ontology.

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 ACADEMIC & RESEARCH BACKGROUND

bioinformatics
bioinformatics
machine learning
machine learning
statistics
statistics
metagenomics
metagenomics