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Relationship between conservation biology and ecology shown through machine reading of 32,000 articles.

ABSTRACT: Conservation biology was founded on the idea that efforts to save nature depend on a scientific understanding of how it works. It sought to apply ecological principles to conservation problems. We investigated whether the relationship between these fields has changed over time through machine reading the full texts of 32,000 research articles published in 16 ecology and conservation biology journals. We examined changes in research topics in both fields and how the fields have evolved from 2000 to 2014. As conservation biology matured, its focus shifted from ecology to social and political aspects of conservation. The 2 fields diverged and now occupy distinct niches in modern science. We hypothesize this pattern resulted from increasing recognition that social, economic, and political factors are critical for successful conservation and possibly from rising skepticism about the relevance of contemporary ecological theory to practical conservation.

SUBMITTER: Hintzen RE

PROVIDER: S-EPMC7317371 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Relationship between conservation biology and ecology shown through machine reading of 32,000 articles.

Hintzen Rogier E RE Papadopoulou Marina M Mounce Ross R Banks-Leite Cristina C Holt Robert D RD Mills Morena M T Knight Andrew A Leroi Armand M AM Rosindell James J

Conservation biology : the journal of the Society for Conservation Biology 20191210 3

Conservation biology was founded on the idea that efforts to save nature depend on a scientific understanding of how it works. It sought to apply ecological principles to conservation problems. We investigated whether the relationship between these fields has changed over time through machine reading the full texts of 32,000 research articles published in 16 ecology and conservation biology journals. We examined changes in research topics in both fields and how the fields have evolved from 2000 ...[more]

PMID: 31702070

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Project description:BackgroundFrozen shoulder (FS) and Dupuytren's disease (DD) are two closely related diseases, but the mechanism of their interaction is unknown. Our study sought to elucidate the molecular mechanism of these two diseases through shared gene and protein interactions.MethodsGSE75152 and GSE140731 data were downloaded from the Gene Expression Omnibus (GEO) database, and shared genes between FS and DD were selected by using R packages. Then, we used Cytoscape software and the STRING database to produce a protein-protein interaction (PPI) network. Important interaction networks and hub genes were selected through MCODE and cytoHubba algorithms. To explore the potential mechanisms of the development of the two diseases, the hub genes were further enriched by GO and KEGG analyses. We predicted the transcription factors (TFs) of hub genes with Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Moreover, we identified candidate genes for FS with DD with cytoHubba and machine learning algorithms. Finally, we analyzed the role of immunocyte infiltration in FS and constructed the relationship between candidate genes and immunocytes in FS.ResultsWe identified a total of 321 shared genes. The results of GO and KEGG enrichment of shared genes showed that extracellular matrix and collagen fibril tissue play a certain role in the occurrence and development of disease. According to the importance of genes, we constructed the key PPI network of shared genes and the top 15 hub genes for FS with DD. Then, we predicted that five TFs are related to the hub genes and are highly expressed in the FS group. Machine learning results show that the candidate genes POSTN and COL11A1 may be key for FS with DD. Finally, immune cell infiltration revealed the disorder of immunocytes in FS patients, and expression of candidate genes can affect immunocyte infiltration.ConclusionWe identified a PPI network, 15 hub genes, and two immune-related candidate genes (POSTN and COL11A1) using bioinformatics analysis and machine learning algorithms. These genes have the potential to serve as diagnostic genes for FS in DD patients. Furthermore, our study reveals disorder of immunocytes in FS.

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Relationship between conservation biology and ecology shown through machine reading of 32,000 articles.

Publications

Relationship between conservation biology and ecology shown through machine reading of 32,000 articles.

Similar Datasets

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