Literature Mining and Ontology BMI/IBGP 730 Autumn, 2010

Literature Mining and OntologyBMI/IBGP 730 Autumn, 2010 Yang Xiang, Ph.D. in Computer Scienceyxiang@bmi.osu.edu Department of Biomedical Informatics The Ohio State University

Outline • What is Literature Mining? • Popular Tools for Literature Mining • Basic Techniques • Indexing: Expediting searching • Linguistic Processing • Other Processing • What is Ontology? • Simple ontology examples • Gene ontology • United Medical Language System • Use and index ontology • Applications of Literature Mining and Ontology

What is Literature (Text) Mining? • The purposes of Literature Mining • Find relevant documents • Discover knowledge (what is knowledge?) • The advantage of computer-based Literature Mining • Simply, computers can search much more documents! • Computers can ‘think’ and discover knowledge. • We will focus on biomedical literature mining in the following

Why Literature Mining is Very Popular in Biomedical Science? • Biomedical science studies nature subjects. • Species • Genes • Phenotypes • Diseases ….

Popular Tools for Biomedical Literature Mining – Document search • Google • Google Scholar: http://scholar.google.com • ISI web of knoledge • www.isiknowledge.com • Pubmed • www.ncbi.nlm.nih.gov/pubmed

Tools for Biomedical Literature Mining – Knowledge discovery • The Gene Ontology • http://www.geneontology.org/ • Gene answer • www.geneanswers.com

Techniques Behind Literature Mining • Interdisciplinary • Computer Science • Information retrieval • Data mining • Natural Language Processing • Machine learning • Library Science • Biomedical Science • Linguistics • Computational linguistics • Statistics • And more! • Two main research areas (some overlaps) • Information Retrieval • Natural Language Processing

Basic Text Search Algorithm text … H e l l o , w o r l d … • Assume text size is n. • Assume search string size is m. • How to design an efficient algorithm to find all matches in the text? • Brutal force algorithm, O(mn). • Boyer-Moore Heuristics, O(mn), but fast in most cases for English text. • KMP (Knuth-Morris-Pratt) algorithm, O(m+n). String to match w o r l d

Information Retrieval (Indexing) • Archiving (preprocessing) documents for fast search • Preprocessing time • Query time • Index size • Accuracy vs relevancy • Precision=|{relevant docs}∩{retrieved docs}|/| {retrieved docs}| • Recall= |{relevant docs}∩{retrieved docs}|/|{relevant docs}| • Fall-out |{nonrelevant docs}∩{retrieved docs}|/|{nonrelevant docs}|

Programming language processing (C++, Java, etc) • Lexical analysis y=x+10; • Syntax analysis assignment operator expression identifier = expression expression + y number identifier x 10

Natural Language Processing • Lexical level • Stemming (including lemmatizing): find the root of a wordswimming, swam, swim, swimmer  swim • Stemming rule may vary (balance between overstemming and understemming) • Typical algorithm (Porter Stemming algorithm) • Alias, Synonym • Grammatical level • Parsing“…We find Gene1 interacts with Gene2…” Sentence Verb phrase Noun phrase Noun phrase Verb Gene1 interact Gene2

Statistical and Data Mining Processing • Statistical • Count the word frequency • Count the expression frequency • Data Mining • Mining the set of frequent words • Association rule

Document Classification (Machine Learning) • E.g., classify all documents related to coffee and health • Various machine learning algorithms can be applied here. Cardioprotective Documents show benefits … Coffee and health related documents Laxative Cholesterol Documents show risk … Anxiety

Ontology • According to philosophy, ontology is a systematic account of Existence • In information science, ontology is a representation of concepts and their relationships, often by directed graphs

Ontology Example (Informal) fish salt water fresh water Asian Europe North American …… native Common Carp Crappie mirror Carp invasive

Ontology Example: Scientifc classification Kingdom Animalia Phylum Chordata … Hemichordata Class … Actinopterygii Sarcopterygii … Neopterygii Subclass Chondrostei Infraclass … Teleostei Order … Cypriniformes Family … Cyprinidae

Gene Ontology (GO) Consortium DNA metabolis cell Molecular function … … … Nucleic acid binding enzyme helicase DNA binding DNA helicase ATP-dependent DNA helicase … Reference: Gene Ontology: tool for the unification of biology, nature genetics, 2000 http://dx.doi.org/ 10.1038/75556

Unified Medical Language System (UMLS) • A compendium of controlled vocabularies in the biomedical sciences (since 1986). It contains: • Metathesaurus • Semantic Network • SPECIALIST Lexicon • Maintained by US National Library of Medicine • Website: http://www.nlm.nih.gov/research/umls/

UMLS - Metathesaurus • Number of biomedical concepts >= 1 million • Number of concept names >=5 million • Stem from over 100 incorporated controlled source vocabularies: • ICD (International Statistical Classification of Diseases and Related Health Problems) • MeSH (Medical Subject Headings) • SNOMED CT (Systematized Nomenclature of Medicine – Clinical Terms) • LOINC (Logical Observation Identifiers Names and Codes) • Gene Ontology • OMIM (Mendelian Inheritance in Man) … http://www.nlm.nih.gov/research/umls/knowledge_sources/metathesaurus/release/source_vocabularies.html

UMLS - Semantic Network • 135 semantic types (categories) • Entity • Physical Object • Organism … … • Event • Actitivity • Behavior … … • 54 semantic relationships (between members of the various Semantic types) • isa • assoicated_with • physically_related_to • part_of… • spatially_related_to • location_of… … http://www.nlm.nih.gov/research/umls/META3_current_semantic_types.html http://www.clres.com/semrels/umls_relation_list.html

Use of ontology systems • Statistical • Gene ontology enrichment test • Indexing • Reachibility • Distance • Path

Represent Ontology by Graphs • Directed Graph • Directed Acyclic Graph (DAG): A good number of ontologiesfall into this type, but not all! • Directed Tree

Reachability The problem: Given two vertices u and v in a directed graph G, is there a path from u to v ? ?Query(1,11) Yes ?Query(3,9) No 15 14 11 13 10 12 6 7 8 9 3 4 5 1 2

Distance The problem: Given two vertices u and v in a (directed) graph G, what is the distance from u to v? ?Query dG(1, 11) =3 15 14 11 13 10 12 6 7 8 9 3 4 5 1 2

Path The problem:Given two vertices u and v in a (directed) graph G, what is a path (are paths) connecting u to v ? 15 14 Find a path from1to11 11 13 10 12 6 7 8 9 3 4 5 1 2

The estimated difficulty of building a very efficient indexing schemes (based on current research) Reference: R. Jin, Y. Xiang, N. Ruan, H. Wang, "Efficiently Answering Reachability Queries on Very Large Directed Graphs", Proc. of ACM SIGMOD Conference, Vancouver, June 9-12, 2008, pp. 595-608. R. Jin, Y. Xiang, N. Ruan, D. Fuhry, "3-HOP: A High-Compression Indexing Scheme for Reachability Query", Proc. of ACM SIGMOD Conference, Providence, Rhode Island, June 29-July 2, 2009, pp. 813-826.

Outline • What is Literature Mining? • Popular Tools for Literature Mining • Basic Techniques • Indexing: Expediting searching • Linguistic Processing • Other Processing • What is Ontology? • Simple ontology examples • Gene ontology • United Medical Language System • Ontology use and indexing • Applications of Literature Mining and Ontology

Applications of Literature Mining and Ontology - I • Build confirmed gene-phenotype relations • Human Phenotype Ontology (HPO) • Built from Online Mendelian Inheritance in Man (OMIM) database. • http://human-phenotype-ontology.org/ Reference: Robinson PN, MundlosS. The Human Phenotype Ontology. Clinical Genetics 77(6) 2010: 525–534. http://dx.doi.org/10.1111/j.1399-0004.2010.01436.x

Applications of Literature Mining and Ontology - II • Predicting unknown gene-phenotype relations • Use text mining to build similarities among phenotypes • Gene relationships are built by protein-protein Interaction databases • Known gene-phenotype relationships can be built by text mining. The previous slide gives an example. • Various methods [Statistical, graph theory, etc.] have been proposed to do the prediction. • Reference: X. Wu, R. Jiang, M.Q. Zhang, and S. Li, Network-based global inference of human disease genes. Molecular Systems Biology, 4(1), 2008 PPI network ≈ G2G network Phenotype similarity graph

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Literature Mining and Ontology BMI/IBGP 730 Autumn, 2010

Literature Mining and Ontology BMI/IBGP 730 Autumn, 2010

Presentation Transcript

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