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Indirect association and ranking hypotheses for literature based discovery
Sam Henry1, Bridget T McInnes2
1Department of Computer Science, Virginia Commonwealth University, 601 W. Main St. Rm 435, Richmond, 23284, USA. henryst@vcu.edu.
BMC Bioinformatics
|August 17, 2019
Summary
Ranking hypotheses in Literature Based Discovery (LBD) is crucial. New indirect association measures, Linking Term Association (LTA), Minimum Weight Association (MWA), and Shared B to C Set Association (SBC), were compared to existing methods.
Area of Science:
- Biomedical Informatics
- Computational Biology
- Data Mining
Background:
- Literature Based Discovery (LBD) generates numerous hypotheses, necessitating automated ranking.
- Traditional association measures often focus on direct relationships, potentially missing indirect but valuable connections.
Purpose of the Study:
- To introduce and evaluate novel indirect association measures for hypothesis ranking in LBD.
- To compare the performance of these new measures against established methods.
Main Methods:
- Proposed indirect association measures: Linking Term Association (LTA), Minimum Weight Association (MWA), and Shared B to C Set Association (SBC).
- Compared against: Linking Set Association (LSA), concept embeddings vector cosine, Linking Term Count (LTC), and direct co-occurrence vector cosine.
- Evaluated using intrinsic (semantic relatedness) and extrinsic (hypothesis ranking on time-slicing datasets) approaches.
Main Results:
- Performance varied across evaluation methods and datasets, indicating potential dataset biases or method-specific strengths.
- Indirect association measures like SBC showed promise in hypothesis ranking.
- Precision and recall curves were generated to assess ranking effectiveness.
Conclusions:
- Linking Term Count (LTC) and Shared B to C Set Association (SBC) emerged as the most suitable methods for LBD hypothesis ranking.
- A diverse set of ranking methods is beneficial for LBD applications.
- Further research is needed to resolve performance discrepancies across different evaluation strategies.
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