1、BioText Conference Birkbeck College, London,Stephen Edwards BSc. University of EdinburghBioNLP meeting 14th November 2005,Speakers,EDIMed SBSS Astra-Zeneca Rob Gaizauskas (Sheffield) BioRAT Andrew Clegg (UoL),EBIMed,Co-occurrence based IE (looking at parse methods) Created on the fly 40% sentences r
2、etrieved useful for PPI Includes navigation to databases (important) Assessible data max 10,000 (moving to full papers)Whatizit modules High speed tagging modules Can be hooked up to any dictionaryRegExp and ML combination should be combined Recall: “The whole truth” Precision: “Nothing but the trut
3、h”,SBSS,Business uses: Patent recognisers IP protection Drug design Author networks, competition and funding Marketing NLP system, statistical linking between concepts External/Internal databases Includes web-sites, forums (negate false rumours!) Microarray - Text-Mining User interface to add new sy
4、nonyms IBM Unstructured Information Management Architecture,Astra-Zeneca,Drug discovery process = masses of data (chem/bio assays, clinical trials, reports etc) Track competition, groups GCLit - gene summaries- MeSH/gene co-occurrences Produce similarity matrices for two genes Back dating to trap no
5、w know associations,Rob Gaizauskas,GO tagging (19,022 terms), GOSlims Many tools: GOPubmed (weighted GO-doc assignment) GO-KDS (comm. Assigns GO terms to PubMed, rubbish!) BLAST - Lit, cluster and structure by GO code AMBIT combines IR/IE Termino module GO, UniProt, UMLS DiscoveryNet data management
6、 software, includes Termino Created complete GO corpus fuzzy match+manual to get GO complete corpus High results achieved assigning GO to abstracts F-measure 0.8 dubious, difficult to replicate evaluation as GO codes incomplete User view applet: GO | Abstracts Glass ceiling, too much tinkering, more
7、 fundamental ideas,Bio Research Assistant (BioRAT),100+ words/sec PhD grads in India pay them! Tagging, PPI extraction, based on GATE (further funding 5 yrs) User defines concepts of interest program defines templates select or reject, most are poor, time costly Or, ML sequence aligns sentences prod
8、uces templates requires less effort but less reliable,NER (Andrew Clegg),Trees discard parts of tree dont need NER achieve max recall then filter through ABNER (high precision) Create every possible variant, strip punctuation, substitute greek, remove stop words, long/short names,MMTx Mapping the UM
9、LS to text,Stephen Edwards BSc. University of EdinburghBioNLP meeting 14th November 2005,Overview,UMLS MMTxHypothesis generationmilkERFuture use,UMLS,Unified Medical Language System Multi-source vocabulary (60 families) 2.5 million terms Concepts in semantic network 12,000,000 relations between conc
10、epts Lexicon Many IDs AUI SUI CUI TUI Customisable (MetaMorphosys),MMTx,Preparatory filtering Relaxed (manual, lexical: 87%) Moderate (relaxed+type-based:75%) Strict (moderate+syntactic) Highly computationally expensive Options restrict to sources Restrict to semantic types Show CUIs, semantic types
11、, treecodes,MMTx parsing,Parsed into noun phrases SPECIALIST minimal commitment parser/MedPost SKR Variant generation Largely preprocessed Candidate retrieval Candidate evaluation Centrality Variation Coverage Cohesiveness Mapping Combines candidates Mapping evaluation (as with candidates),Sentence:
12、 0|0|183|Progress is described on the advanced stages in design of an instrument for the study of red blood cell aggregation and blood viscosity under near-zero gravity conditions.|11540609:1Phrase: “Progress“ Meta Mapping (1000)1000 C1280477:Progress Functional Concept Phrase: “is“ Meta Candidates
13、(0): Meta Mappings: Phrase: “described“ Meta Candidates (0): Meta Mappings: Phrase: “on the advanced stages“ Meta Mapping (888)694 C0205179:Advanced Qualitative Concept 861 C1306673:Stages Functional Concept Phrase: “in design“ Meta Candidates (0): Meta Mappings: Phrase: “of an instrument“ Meta Mapp
14、ing (1000)1000 C0348000:Instrument, NOS Manufactured Object Phrase: “for the study“ Meta Mapping (1000)1000 C0008972:Study (Clinical Research) Research Activity Meta Mapping (1000)1000 C0557651:Study Manufactured Object Phrase: “of red blood cell aggregation“ Meta Mapping (916)756 C0014792:Blood Cel
15、l, Red (Erythrocytes) Cell 812 C0332621:Aggregation, NOS Functional Concept ,MMTx customisation,Advised to customise English only sources used Removed inappropriate sources 2secs/sentence (12 X improved performance) Can limit to sources, semantic types Running on Windows, FC2 Linux Lots of fudging r
16、equired!,Hypothesis generation,Aim to extract interactions and diseases Swanson (Fish oil Blood viscosity - Raynauds disease) Srinivasan (Turmeric - NFB - Chrons Disease) Weeber (Thalidamide IL-4 Pancretitis) Confirmed experimentally,Hypothesis generation,Open/Closed Co-occurrence relationship extra
17、ctionA (Raynauds Disease) B1 B2 B3 (Blood Viscosity) B4,Hypothesis generation,B3 (Blood viscosity) C1 C2 C3 (Fish Oil) C4,Hypothesis generation,Closed A B1 B2 B3 B4,C B5 B2 B6 B1 ,Need to remove known A C relationships,Other systems,ManJal MeSH only, basic LitLinker shows associations by frequency T
18、ransMiner can be linked to MicroArray DAD Drug Adverse Drug Reactionsi-HOP slick informative sentences (e.g. experimental evidence, synonyms, hyperlinked BUT 5 species only)(Refs cited at end),Other systems,ManJal MeSH only, basic LitLinker shows associations by frequency TransMiner can be linked to
19、 MicroArray DAD Drug Adverse Drug Reactionsi-HOP slick informative sentences (e.g. experimental evidence, synonyms, hyperlinked BUT five species only) linkouts to external databases EBIMed(Refs cited at end),milkER program,Manjal DAD,milkER program,Input MEDLINE A/B/C term,Extract Titles, Abstracts,
20、 MeSH, Substance Terms,Tag milk proteins/peptides or term in title and abstracts,UMLS tagging (customised MMTx),Sort and count MeSH and Substance terms,Extract sentences containing the protein/peptide or term,Partial standardisation, remove overmatching,Select B terms for subsequent analysis,Group t
21、erms by concept (removes plurals and variants),Remove parent or child terms of search term,Remove terms that are too general on second or third level of the UMLS heiracrchy,Remove over-abundant terms e.g. 15,000 documents,Calculate weighting of term -TF*IDF Level of support of relationship (e.g. Mus
22、t occur in 5 titles with A term or is spurious ),Cluster concepts by using the head of the noun(? E.g. common and right migraine),Filter and sort terms Physiological function Entity Location Combined No filter (filter by MMTx weight?),Variable parameters,Features,User defined gazetteer Removes overm
23、atches casein kinase Currently hard-coded Some standardisation E.g. alpha-CN = alpha casein prevents loss of data from MMTx Each sent/title given unique ID Main MeSH terms, MeSH terms, Substance terms Can use any PubMed query, PMID etc Did you mean?,Comparative filtration,Compare filter combinations
24、 Calibrate with known link (RD Fish Oil) Highest rank of blood viscosity Dependence on topic? Combine type ranks MeSH terms Substances terms Title Abstract,Targets,Milk proteins Largely digested Maternal regulation Milk peptides Can reach blood stream, stable Receptor binding Protein binding Immunor
25、esponse,Targets,Plasmin remodelling Plasmin levels increase during parturition and involution Hypothesis: peptides involved in restructure Extension: Are peptides involved in apoptosis, hyperplasia? Role of the abundant proteinsMFGL Xanthine Oxidase CD36 -lactoglobulin,Information kept,Defined area
26、(milk) therefore can store detailed info., unlike generic system Known assoc with strength Unknown assoc with strength LinkOuts Main MeSH terms MeSH terms Substance terms MMTx concepts,Problems,No directionality on relationships Incorrect MMTx tagging Peptide literature Small(ish) amount of named pe
27、ptide data Need to TM peptides, however, also strength as more disparate data Species/age differentiation (by MeSH?),Conclusions,Co-occurrence relationships derived for milk protein/peptides and other terms Hypothesis generation to identify new knowledge Information stored for user access,Future wor
28、k,Debug! Species/age specificity by MeSH term? Check incorrect MMTx tagging add bioactive peptides to source data Link proteins to milkER sequence database Finish user interface Learn Java ,Acknowledgements,Prof. Lindsay Sawyer Dr. Carl Holt (Hannah Research Institute, Ayr) Prof. Bonnie Webber (Info
29、rmatics)Dr. Alistair Kerr and Gail Sinclair technical support,Miscellaneous,ArrayPaths, Stratagene Huang et al., 2005 PPI extractor program Metis (Mitchell et al) flags interesting sentences to user from a UniProt sequence search, crap but nice to have BLAST MELISA (Abasolo et al) ontology based IE
30、Genomes to Systems Conference Manchester, 22 - 24th March 2006,References,Abasolo JM, Gomez M: MELISA. An ontology-based agent for information retrieval in medicine. ECDL Workshop on the Semantic Web 2000. Aronson AR: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap progra
31、m. Proc AMIA Symp 2001:17-21. Aronson AR: Filtering the UMLS Metathesaurus for MetaMap. 2001. Bodenreider O: The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res 2004, 32(Database issue):D267-270. iHOP (Information Hyperlinked over Proteins) http:/b.uam.e
32、s/UniPub/iHOP/ Hoffmann R, Valencia A: Implementing the iHOP concept for navigation of biomedical literature. Bioinformatics 2005, 21 Suppl 2:ii252-ii258. Mitchell AL, Divoli A, Kim JH, Hilario M, Selimas I, Attwood TK: METIS: multiple extraction techniques for informative sentences. Bioinformatics
33、2005, 21(22):4196-4197. Narayanasamy V, Mukhopadhyay S, Palakal M, Potter DA: TransMiner: mining transitive associations among biological objects from text. J Biomed Sci 2004, 11(6):864-873. Pratt W, Yetisgen-Yildiz M: LitLinker: Capturing Connections Across the Biomedical Literature. K-CAP 2003 200
34、3.,References (2),Pratt W, Yetisgen-Yildiz M: A study of biomedical concept identification: MetaMap vs. people. AMIA Annu Symp Proc 2003:529-533. EBIMed http:/www.ebi.ac.uk/Rebholz-srv/ebimed/index.jsp Whatizit http:/www.ebi.ac.uk/Rebholz-srv/whatizit Shatkay H: Hairpins in bookstacks: information r
35、etrieval from biomedical text. Brief Bioinform 2005, 6(3):222-238. Srinivasan P: Text mining: Generating hypotheses from MEDLINE. J Am Soc Inf Sci Technol 2004, 55(5):396-413. Srinivasan P, Libbus B: Mining MEDLINE for implicit links between dietary substances and diseases. Bioinformatics 2004, 20 S
36、uppl 1:I290-I296. Weeber M, Klein H, Aronson AR, Mork JG, de Jong-van den Berg LT, Vos R: Text-based discovery in biomedicine: the architecture of the DAD-system. Proc AMIA Symp 2000:903-907. Weeber M, Klein H, de Jong-van den Berg LTW, Vos R: Using concepts in literature-based discovery: Simulating
37、 Swansons Raynaud-fish oil and migraine-magnesium discoveries. J Am Soc Inf Sci Technol 2001, 52(7):548-557. Weeber M, Vos R, Klein H, de Jong-van den Berg LTW, Aronson AR, Molema G: Generating hypotheses by discovering implicit associations in the literature: A case report of a search for new potential therapeutic uses for thalidomide. J Am Med Inf Assoc 2003, 10(3):252-259. Wren JD: Extending the mutual information measure to rank inferred literature relationships. BMC Bioinformatics 2004, 5:145.,
