Machine learning with the hierarchy-of-hypotheses (HoH) approach discovers novel pattern in studies on biological invasions

  • Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study thatResearch synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.show moreshow less

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Metadaten
Author details:Masahiro RyoORCiD, Jonathan M. JeschkeORCiDGND, Matthias C. RilligORCiD, Tina HegerORCiDGND
DOI:https://doi.org/10.1002/jrsm.1363
ISSN:1759-2879
ISSN:1759-2887
Pubmed ID:http://www.ncbi.nlm.nih.gov/pubmed?term=31219681
Title of parent work (English):Research synthesis methods
Publisher:Wiley
Place of publishing:Hoboken
Publication type:Article
Language:English
Date of first publication:2019/06/20
Completion year:2020
Release date:2021/06/03
Tag:artificial intelligence; hierarchy-of-hypotheses approach; machine learning; meta-analysis; synthesis; systematic review
Volume:11
Issue:1
Page number:8
First page:66
Last Page:73
Funding institution:Bundesministerium fur Bildung und ForschungFederal Ministry of Education & Research (BMBF) [01LC1501A]; Japan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [JE 288/9-1, JE 288/9-2]; German Federal Ministry of Education and Research (BMBF)Federal Ministry of Education & Research (BMBF) [01LC1501A]; Volkswagen FoundationVolkswagen [Az 92807]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie
DDC classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Peer review:Referiert
Publishing method:Open Access / Hybrid Open-Access
License (German):License LogoCC BY - Namensnennung, 4.0 International