ContentMine + EPMC: Finding Zika!
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Published on Feb 07, 2016 by PMR Use of ContentMine tools on the Open Access subset of EuropePubMedCentral to discover new knowledge about the Zika virus. Includes clips of the software in action
![Content Mine + Europe PubMedCentral
Peter Murray-Rust,
ContentMine.org and UniversityOfCambridge
Wellcome Trust, London, UK 2016-02-08
Getpapers[0] and AMI[1]download and analyze papers from
EuropePubMedCentral
[0][1] F/OSS tools from contentmine.org](https://image.slidesharecdn.com/zikaepmc-160207213745-160209121732/85/ContentMine-EPMC-Finding-Zika-1-320.jpg)
![Automated Semantic Fulltext
• EuropePMC provides coherent OpenAccess
• getpapers: wrapper for repos and search engines.
• AMI filters, checks[1], transforms facts in papers. Here:
– Sequences in text
– Species and genera
– Genes
– User dictionaries
– (RRIDs, chemistry, places, phylo)
[0] All operations shown run in total of <3 minutes.
[1] Dictionaries and lookup.
[2] Usable from home by anyone](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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ContentMine + EPMC: Finding Zika!
- 1. Content Mine + Europe PubMedCentral Peter Murray-Rust, ContentMine.org and UniversityOfCambridge Wellcome Trust, London, UK 2016-02-08 Getpapers[0] and AMI[1]download and analyze papers from EuropePubMedCentral [0][1] F/OSS tools from contentmine.org
- 2. Automated Semantic Fulltext • EuropePMC provides coherent OpenAccess • getpapers: wrapper for repos and search engines. • AMI filters, checks[1], transforms facts in papers. Here: – Sequences in text – Species and genera – Genes – User dictionaries – (RRIDs, chemistry, places, phylo) [0] All operations shown run in total of <3 minutes. [1] Dictionaries and lookup. [2] Usable from home by anyone
- 3. catalogue getpapers query Daily Crawl EPMC, arXiv CORE , HAL, (UNIV repos) ToC services PDF HTML DOC ePUB TeX XML PNG EPS CSV XLSURLs DOIs crawl quickscrape norma Normalizer Structurer Semantic Tagger Text Data Figures ami UNIV Repos search Lookup CONTENT MINING Chem Phylo Trials Crystal Plants COMMUNITY plugins Visualization and Analysis PloSONE, BMC, peerJ… Nature, IEEE, Elsevier… Publisher Sites scrapers queries taggers abstract methods references Captioned Figures Fig. 1 HTML tables 30, 000 pages/day Semantic ScholarlyHTML Facts CONTENTMINE Complete OPEN Platform for Mining Scientific Literature
- 4. Download all Open Access “Zika” from EuropePMC in 10 seconds (click below for movie)
- 5. Downloaded all Open Access “Zika” from EuropePMC in 10 seconds Final download screen
- 6. Eyeballing 20/120 Zika papers, click below for movie
- 7. 3011 virus 1939 Ae./Aedes 1212 dengue 901 mosquito/es 894 species 791 ZIKV 721 using 716 DENV 567 detection 513 aegypti 484 infection 442 RNA 428 protein 401 albopictus 360 viral Commonest words in 120 Zika papers
- 8. Filtering local files for sequence and viruses AMI (part of ContentMine software)
- 9. DNA Primers found in running text
- 10. Commonest species in 120 Zika papers 335 Ae. aegypti 205 Ae. albopictus 128 Aedes albopictus 88 Aedes aegypti 63 Ae. bromeliae 58 Ae. lilii 46 Ae. hensilli 42 Glossina pallidipes 40 Plasmodium vivax 35 Ae. luteocephalus 28 Ae. vittatus 25 Ae. furcifer 22 Plasmodium falciparum 21 Drosophila melanogaster
- 11. 183 Wolbachia 70 Aedes 49 Flavivirus 30 Glossina 29 Crataegus 20 Flaviviridae 17 Culex 16 Drosophila 11 Ochlerotatus 10 Alphavirus 9 Musca 9 Phlebovirus 6 Plasmodium Commonest genera in Zika papers
- 12. Example EMPC/ContentMine • microcephaly 400/2400 papers; 2 mins; commonest genes: 203 MCPH1 86 MECP2 54 SOX2 49 E2F1 47 SNAP29 40 IKBKG 40 NDE1
- 13. Further directions • With Hypothes.is to use ContentMine results to annotate literature. • With Cambridge Univ Library extracting daily scientific facts from open and closed literature. • Working with EBI. • Running workshops, hackdays, in bio-science and anything else you want.
Notas do Editor
- Hi, I’m here to talk about AMI; a data extraction framework and tool. First, I just want highlight some of key contributors to the projects; Andy for his work on the ChemistryVisitor and Peter for the overall architecture. In this talk, I’m going to impress the importance of data in a specific format and its utility to automated machine processing. Then I’m going to demonstrate AMI’s architecture and the transformation of data as it flows through the process. I’m going to dwell a little on a core format used, Scalable Vector Graphics (SVG) before introducing the concept of visitors, which are pluggable context specific data extractors. Next, I’m going to introduce Andy’s ChemVisitor, for extracting semantic chemistry data, along with a few other visitors that can process non-chemistry specific data. Finally, I will demonstrate some uses of the ChemVisitor, within the realm of validation and metabolism.