Download to read offline
![Semantic Fulltext
• EuropePMC coherent OpenAccess
• getpapers: query , download (through API).
• AMI filters, checks[1], transforms facts in papers.
• sequences, species, genera, genes,
dictionaries
[0] All operations shown run in total of <3 minutes.
[1] Dictionaries and lookup.
[2] Usable from home by anyone
Zika endemic areas
Wikimedia CC-BY-SA](https://image.slidesharecdn.com/ftdmleiden-160229142301-160229153258/75/Content-Mining-of-Science-and-Medicine-6-2048.jpg)
![DNA Primers in running text
…the sodium channel voltage dependent gene (Nav). Primers
used to amplify this fragment were AaNaA
5’-ACAATGTGGATCGCTTCCC-3’
and AaNaB 5’-TGGACAAAAGCAAGGCTAAG-3’(8).
The primers amplify a fragment of approximately 472…
Snippet (quotable under 2014 UK Statutory Instrument (“Hargreaves”):
~/PMC4654492/results/sequence/dnaprimer/results.xml”
W3C Annotation
[PREFIX]
[MATCH] (link to target)
[SUFFIX]
CMine structure
plugin
option
DNA double stranded fragment
Wikimedia CC-BY-SA](https://image.slidesharecdn.com/ftdmleiden-160229142301-160229153258/75/Content-Mining-of-Science-and-Medicine-12-2048.jpg)
Uploaded byTheContentMine
Content Mining of Science and Medicine
The document outlines the content mining strategies and tools developed by ContentMine to extract and analyze scientific literature, emphasizing the importance of open access and the right to mine data for research. It provides insight into the processing of scientific papers, enabling rapid access to essential information and trends in fields like biology and medicine. The presentation discusses various components of text and data mining, including crawling, scraping, and analyzing content from numerous sources.
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Content Mining of Science and Medicine
- 1. Content Mining ofScience and Medicine Peter Murray-Rust, ContentMine.org and UniversityofCambridge FTDM Knowledge Cafe, Leiden, NL, 2016-02-29 F/OSS tools from contentmine.org Images from Wikimedia CC-BY-SA
- 2. Disclaimer The opinions, softwareand objects in this presentation are those of PMR+ContentMine (CM), in its non-FutureTDM role. No FTDM resources were used in creating slides, software, artefacts. PMR has tried to give an objective listing of most of the main components of TDM, but has used CM technology to illustrate this.
- 3. The Right toRead is the Right to Mine**PeterMurray-Rust, 2011 http://contentmine.org
- 4. Mining strategy • Discover.negotiate permissions . => bibliography • Crawl / Scrape (download), documents AND supplemental • Normalize. PDF => XML • Index: facets => Facts and snippets (“entities”) • Interpret/analyze entities => relationships, aggregations (“Transformative”) • Publish
- 5. 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
- 6. Semantic Fulltext • EuropePMCcoherent OpenAccess • getpapers: query , download (through API). • AMI filters, checks[1], transforms facts in papers. • sequences, species, genera, genes, dictionaries [0] All operations shown run in total of <3 minutes. [1] Dictionaries and lookup. [2] Usable from home by anyone Zika endemic areas Wikimedia CC-BY-SA
- 7. Download all OpenAccess “Zika” from EuropePMC in 10 seconds (click below for movie) Aedes aegypti, Wikimedia CC-BY-SA Note: movies of this and other slides can be seen at https://vimeo.com/154705161
- 8. Downloaded all OpenAccess “Zika” from EuropePMC in 10 seconds Final download screen
- 9. Eyeballing 20/120 Zikapapers, click below for movie Yellow Fever Virus Wikimedia CC-BY-SA Note: movie of this and other slides can be seen at https://vimeo.com/154705161
- 10. 3011 virus 1939 Ae./Aedes 1212dengue 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 Mosquito spp. Wikimedia CC-BY-SA
- 11. Filtering local filesfor sequence and viruses AMI (part of ContentMine software) (click below for movie) Note: movies of this and other slides can be seen at https://vimeo.com/154705161
- 12. DNA Primers inrunning text …the sodium channel voltage dependent gene (Nav). Primers used to amplify this fragment were AaNaA 5’-ACAATGTGGATCGCTTCCC-3’ and AaNaB 5’-TGGACAAAAGCAAGGCTAAG-3’(8). The primers amplify a fragment of approximately 472… Snippet (quotable under 2014 UK Statutory Instrument (“Hargreaves”): ~/PMC4654492/results/sequence/dnaprimer/results.xml” W3C Annotation [PREFIX] [MATCH] (link to target) [SUFFIX] CMine structure plugin option DNA double stranded fragment Wikimedia CC-BY-SA
- 13. Commonest species in120 Zika papers 423 Ae./Aedes aegypti 333 Ae./Aedes albopictus 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 pre=“fever (DHF), are caused by the world's most prevalent mosquito-borne virus. 37 DENV is carried by " exact="Aedes aegypti” post=" mosquito, which is strongly affected by ecological and human drivers, but also influenced by clima" name="binomial"/>
- 14. 183 Wolbachia 70 Aedes 69Flavivirus/Flaviviridae 30 Glossina 17 Culex Commonest genera in Zika papers pre=”…-negative endosymbiotic bacterium, is a promising tool against diseases transmitted by mosquitoes. " exact="Wolbachia” post=" can be found worldwide in numerous arthropod species. More than 65% of all insect species are natu…” Wolbachia in insect cell Wikimedia CC-BY-SA
- 15. 38 ITS 20 MHC2TA 19COI 14 CYPJ92 5 CYP6BB2 4 CYP9J28 3 MHC Commonest genes in 120 Zika papers
- 16. • microcephaly 400/2400papers; 2 mins; commonest genes: 203 MCPH1 86 MECP2 54 SOX2 49 E2F1 47 SNAP29 40 IKBKG 40 NDE1 N-terminal domain of microcephalin Wikimedia CC-BY-SA
- 17. Systematic Reviews Researchers andtheir machines need to “read” hundreds of papers a day or even more.
- 18. Polly has 20seconds to read this paper… …and 10,000 more
- 19. ContentMine software cando this in a few minutes Polly: “there were 10,000 abstracts and due to time pressures, we split this between 6 researchers. It took about 2-3 days of work (working only on this) to get through ~1,600 papers each. So, at a minimum this equates to 12 days of full-time work (and would normally be done over several weeks under normal time pressures).”
- 20. 400,000 Clinical Trials In10 government registries Mapping trials => papers http://www.trialsjournal.com/content/16/1/80 2009 => 2015. What’s happened in last 6 years?? Search the whole scientific literature For “2009-0100068-41”
- 21.
- 22. Mining strategy • Discover.negotiate permissions . => bibliography • Crawl / Scrape (download), documents AND supplemental • Normalize. PDF => XML • Index: facets => Facts and snippets (“entities”) • Interpret/analyze entities => relationships, aggregations (“Transformative”) • Publish
- 23. What is “Content”? http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.01113 03&representation=PDFCC-BY SECTIONS MAPS TABLES CHEMISTRY TEXT MATH contentmine.org tackles these
- 24. catalogue getpapers query Daily Crawl EuPMC, 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
- 25.
- 26. Open Content Miningof FACTs Machines can interpret chemical reactions We have done 500,000 patents. There are > 3,000,000 reactions/year. Added value > 1B Eur.
- 27. Facts in context dailyIUCN endangered species news en.wikipedia.org CC By-SA
- 28. ContentMine Fact ofThe Day • Fact of the day • Endangered species in recent science • Facts • Bubbles
- 29.
- 30.
- 31. OCR (Tesseract) Norma (imageanalysis) (((((Pyramidobacter_piscolens:195,Jonquetella_anthropi:135):86,Synergistes_jonesii:301):131,Thermotoga _maritime:357):12,(Mycobacterium_tuberculosis:223,Bifidobacterium_longum:333):158):10,((Optiutus_te rrae:441,(((Borrelia_burgdorferi:…202):91):22):32,(Proprinogenum_modestus:124,Fusobacterium_nucleat um:167):217):11):9); Semanticre-usable/computable output (ca 4 secs/image)
- 32. Supertree for 924species Tree
- 33. Supertree created from4300 papers
- 34. ContentMine working withLibraries • Cambridge: Library, Plant Sciences, Epidemiology, Chemistry • Cochrane Collaboration on Systematic Reviews of Clinical Trials • FutureTDM (H2020, LIBER) • Running workshops and training • Offers services for information extraction and indexing for born-digital documents.
- 35. CM Future • Hypothes.isuse ContentMine results for annotation • (with Cambridge Univ Library) extracting daily scientific facts from open and closed literature. • with EBI, Cochrane Collaborations, JISC, OKF, LIBER, TGAC/JohnInnes, DNADigest. • Running workshops, hackdays. • Planned outreach: MEPs, EC, Slashdot, Reddit, Kickstarter, geekdom • http://contentmine.org (OpenLock non-profit)
- 36. The Right toRead is the Right to Mine**PeterMurray-Rust, 2011 http://contentmine.org
Editor's Notes
- #2 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.