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High Throughput Co-culture Assays for the Investigation of Microbial Interactions
Published on: October 15, 2019
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@MInter: automated text-mining of microbial interactions.
Kun Ming Kenneth Lim1, Chenhao Li2, Kern Rei Chng3
1Computational and Systems Biology, Genome Institute of Singapore, Singapore 138672, Singapore Computational Biology Program, Faculty of Science.
Bioinformatics (Oxford, England)
|June 18, 2016
Summary
Automated information extraction system @MInter identifies microbial interactions from scientific literature. This tool aids in understanding microbial communities by efficiently processing dispersed data, reducing manual curation efforts.
Area of Science:
- Microbiology
- Bioinformatics
- Computational Biology
Background:
- Microbial consortia functions are driven by numerous interactions.
- Information on microbial interactions is scattered across scientific literature.
- Manual data collation is time-consuming and impractical.
Purpose of the Study:
- To develop an automated system for extracting microbial interaction information from text.
- To create a tool that facilitates the study of microbial communities by making interaction data accessible.
Main Methods:
- Developed @MInter, an automated information extraction system using Support Vector Machines.
- Trained and tested @MInter on a curated dataset of 735 species interactions and 3917 annotated abstracts.
- Applied cross-validation to assess system performance.
Main Results:
- @MInter achieved high specificity (95%) and AUC (0.97) in detecting abstracts with microbial interactions.
- The system demonstrated high recall (95%) but lower precision (25%) at the interaction level.
- @MInter reduced annotator workload 13-fold and identified 357 interactions for human skin bacteria.
Conclusions:
- @MInter is an effective tool for automated extraction of microbial interaction data.
- The system aids in understanding complex microbial communities by consolidating literature information.
- @MInter significantly improves the efficiency of biological data analysis.
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