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The primate infraorder Simiiformes, comprising Old and New World monkeys and apes, includes the most well-studied species on earth. Their most comprehensive molecular timetree, assembled from thousands of published studies, is found in the TimeTree database and contains 268 simiiform species. It is, however, missing 38 out of 306 named species in the NCBI taxonomy for which at least one molecular sequence exists in the NCBI GenBank. We developed a three-pronged approach to expanding the timetree of Simiiformes to contain 306 species. First, molecular divergence times were searched and found for 21 missing species in timetrees published across 15 studies. Second, untimed molecular phylogenies were searched and scaled to time using relaxed clocks to add four more species. Third, we reconstructed ten new timetrees from genetic data in GenBank, allowing us to incorporate 13 more species. Finally, we assembled the most comprehensive molecular timetree of Simiiformes containing all 306 species for which any molecular data exists. We compared the species divergence times with those previously imputed using statistical approaches in the absence of molecular data. The latter data-less imputed times were not significantly correlated with those derived from the molecular data. Also, using phylogenies containing imputed times produced different trends of evolutionary distinctiveness and speciation rates over time than those produced using the molecular timetree. These results demonstrate that more complete clade-specific timetrees can be produced by analyzing existing information, which we hope will encourage future efforts to fill in the missing taxa in the global timetree of life.
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Discovering research articles containing evolutionary timetrees by machine learning
Abstract MotivationTimetrees depict evolutionary relationships between species and the geological times of their divergence. Hundreds of research articles containing timetrees are published in scientific journals every year. The TimeTree (TT) project has been manually locating, curating and synthesizing timetrees from these articles for almost two decades into a TimeTree of Life, delivered through a unique, user-friendly web interface (timetree.org). The manual process of finding articles containing timetrees is becoming increasingly expensive and time-consuming. So, we have explored the effectiveness of text-mining approaches and developed optimizations to find research articles containing timetrees automatically. ResultsWe have developed an optimized machine learning system to determine if a research article contains an evolutionary timetree appropriate for inclusion in the TT resource. We found that BERT classification fine-tuned on whole-text articles achieved an F1 score of 0.67, which we increased to 0.88 by text-mining article excerpts surrounding the mentioning of figures. The new method is implemented in the TimeTreeFinder (TTF) tool, which automatically processes millions of articles to discover timetree-containing articles. We estimate that the TTF tool would produce twice as many timetree-containing articles as those discovered manually, whose inclusion in the TT database would potentially double the knowledge accessible to a wider community. Manual inspection showed that the precision on out-of-distribution recently published articles is 87%. This automation will speed up the collection and curation of timetrees with much lower human and time costs. Availability and implementationhttps://github.com/marija-stanojevic/time-tree-classification. Supplementary informationSupplementary data are available at Bioinformatics online.
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- PAR ID:
- 10394306
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Bioinformatics
- Volume:
- 39
- Issue:
- 1
- ISSN:
- 1367-4803
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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{"Abstract":["The relationship between physical activity and mental health, especially depression, is one of the most studied topics in the field of exercise science and kinesiology. Although there is strong consensus that regular physical activity improves mental health and reduces depressive symptoms, some debate the mechanisms involved in this relationship as well as the limitations and definitions used in such studies. Meta-analyses and systematic reviews continue to examine the strength of the association between physical activity and depressive symptoms for the purpose of improving exercise prescription as treatment or combined treatment for depression. This dataset covers 27 review articles (either systematic review, meta-analysis, or both) and 365 primary study articles addressing the relationship between physical activity and depressive symptoms. Primary study articles are manually extracted from the review articles. We used a custom-made workflow (Fu, Yuanxi. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection that uses the Scopus API and manual work to extract and disambiguate authorship information for the 392 reports. The author information file (author_list.csv) is the product of this workflow and can be used to compute the co-author network of the 392 articles.\r\n\r\nThis dataset can be used to construct the inclusion network and the co-author network of the 27 review articles and 365 primary study articles. A primary study article is "included" in a review article if it is considered in the review article's evidence synthesis. Each included primary study article is cited in the review article, but not all references cited in a review article are included in the evidence synthesis or primary study articles. The inclusion network is a bipartite network with two types of nodes: one type represents review articles, and the other represents primary study articles. In an inclusion network, if a review article includes a primary study article, there is a directed edge from the review article node to the primary study article node. The attribute file (article_list.csv) includes attributes of the 392 articles, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network.\r\nCollectively, this dataset reflects the evidence production and use patterns within the exercise science and kinesiology scientific community, investigating the relationship between physical activity and depressive symptoms.\r\n\r\nFILE FORMATS\r\n1.\tarticle_list.csv - Unicode CSV\r\n2.\tauthor_list.csv - Unicode CSV\r\n3.\tChinese_author_name_reference.csv - Unicode CSV\r\n4.\tinclusion_net_edges.csv - Unicode CSV\r\n5.\treview_article_details.csv - Unicode CSV\r\n6.\tsupplementary_reference_list.pdf - PDF\r\n7. README.txt - text file\r\n\r\nUPDATES IN THIS VERSION COMPARED TO V1(Clarke, Caitlin; Lischwe Mueller, Natalie; Joshi, Manasi Ballal; Fu, Yuanxi; Schneider, Jodi (2022): The Inclusion Network of 27 Review Articles Published between 2013-2018 Investigating the Relationship Between Physical Activity and Depressive Symptoms. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4614455_V1)\r\nIn V1, we did not upload the file "article_list.csv." We uploaded the missing file in this version, and everything else remains the same."]}more » « less
