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Information Extraction from Literature Yue Lu BeeSpace Seminar Oct 24, 2007 Outline Overview of BeeSpace v4 Entity Recognition Relation Extraction Overview BeeSpace V4 deeper semantic base than the current v3 system entities and relations VS mutual information Four levels Level1: Entity Recognition Level2: Entity Association Mining Level3: Relation Extraction Level4: Inference and Hypothesis Generation Overview Level1: Entity Recognition (detailed later) Level2 Entity Association Mining Suppose entities are properly tagged Utilize the co-occurrence patterns of entities to extract semantics e.g. a bee biologist may want to know which genes are important for foraging behavior. Similar to TREC Genomics 2007 task TREC Genomics 2007 e.g. “Which [PATHWAYS] are possibly involved in the disease ADPKD?” currently only retrieval techniques Gene synonym expansion Conjunctive query interpretation User relevance feedback tagged Entities definitely would help Overview Level3: Relation Extraction Goal is to extract the relations between entities Generally requires entities to be properly tagged first Detailed later Level4: Inference and Hypothesis Generation Inference on knowledge base Graph mining Outline Overview of BeeSpace v4 Entity Recognition Relation Extraction Entity Recognition Gene Example: Although <GENE>mxp</GENE> and <GENE>Pb</GENE> display very similar expression patterns, <GENE>pb</GENE> null embryos develop normally Entity Recognition Anatomy Example: In normal embryos, mxp is expressed in the <ANATOMY>maxillary</ANATOMY> and <ANATOMY>labial</ANATOMY> segments, whereas ectopic expression is observed in some GOF variants. Entity Recognition Biological process Example: Amongst these are the Bicoid, the Nanos, and the terminal class gene products, some of which are oncoproteins involved in signal transduction for <BIOLOGICAL PROCESS>the formation of terminal structures in the embryo<BIOLOGICAL PROCESS>. Entity Recognition Pathways Example: Several signal transduction pathways have been described in Drosophila, and this review explores the potential of oncogene studies using one of those pathways - <PATHWAY>the terminal class signal transduction pathway</PATHWAY> - to better understand the cellular mechanisms of protooncogenes that mediate cellular responses in vertebrates including humans Entity Recognition Protein family Example: While non-arthropod orthologs have been found for many Drosophila eye developmental genes, this has not been the case for the glass (gl) gene, which encodes a <PROTEIN FAMILY>zinc finger transcription factor</PROTEIN FAMILY> required for photoreceptor cell specification, differentiation, and survival. Entity Recognition CRE (cis-regulatory elements) Example: A synthetic, 23-bp <CRE>ecdysterone regulatory element (EcRE) </CRE>, derived from the upstream region of the Drosophila melanogaster hsp27 gene, was inserted adjacent to the herpes simplex virus thymidine kinase promoter fused to a bacterial gene for chloramphenicol acetyltransferase (CAT). Entity Recognition Phenotype Definition: a set of observable physical characteristics of an individual organism Example: Fog, dumpy Entity Recognition Class1: Small Variation (Dictionary/Ontology) Organism, Anatomy , Biological Process, Pathway, Protein Family Class2: Medium Variation Gene, cis Regulatory Element Class3: Large Variation Phenotype, Behavior Entity Recognition Generally can be defined as a classification problem Boils down to feature definition Class1: matching a word in the Dictionary/Ontology Class2: prefix/suffix of the word, POS tags, … Class3:? Entity Recognition Firstly focus on Class1 Relatively simple Class2 and Class3 need training examples Useful in entity association mining Useful in facilitating extraction of many interesting relations Related work: Textpresso Textpresso Input: full text C. elegans literature Output: tagged XML format Defined a Textpresso ontology First category is biological entities manually curated a lexicon of names Implemented by PERL regular expressions We could reuse some of the regular expressions Entity Recognition Resources: Organism Anatomy Entrez gene table, Textpresso, BeeSpace DB FlyBase Biological Process, Textpresso Cellular Component, Molecular Function Pathway KEGG Protein Family PDB, NCBI Outline Overview of BeeSpace v4 Entity Recognition Relation Extraction Relation Extraction Expression Location the expression of a gene in some location (tissues, body parts) Homology/Orthology one gene is homologous to another gene Relation Extraction Biological process one gene has some role in a biological process Genetic/Physical/Regulatory Interaction one gene interacts with another gene in a certain fashion (3 types of relations) a simple case: Protein-Protein Interaction (PPI) Relation Extraction Generally can be defined as a classification problem, which requires training data Domain adaptation? an example of PPI PPI Problem Definition: Gene/protein names are already tagged A known list of interaction words 133 words classify each tuple (p1, p2, interWord) in one single sentence PPI Methods Learning algorithm: Maximum Entropy Context features “Extracting protein-protein interactions using simple contextual features training data” BioNLP Workshop on HLT-NAACL 06 e.g. lexical forms, POS tags … Less dependent on domain PPI Training/Testing data: BioCreative 1000 hand labeled sentences, 3964 tuples 5-fold cross validation Performance avgpr = 47.14624 avgre = 43.97337 avgf1 = 45.35523 PPI Training data: BioCreative 1000 Testing Data (different domain) Bee hand labeled sentences, 3964 tuples collection Performance (Judged by Moushumi) Total number of tuples extracted as PPI instances: 92 Precision: 63% PPI Misclassification examples Type1: No interaction Sentence: Pretreatment of platelet suspension with phospholipase A2 from N. naja atra or A. mellifera venom (50 .mu.g/ml) inhibited platelet aggregation induced by sodium arachidonate or collagen, but not induced by thrombin or ionophore A-23187. False: (collagen, thrombin, induced) True: relation between protein and platelet aggregation; no PPI PPI Misclassification examples Type2: Incorrect interaction word Sentence: IgG antibody was able to inhibit binding of IgE antibody in the PLA radioallergsorbent test (RAST) from 10-40% at a molar excess of 10- to 1000-fold. False: (IgG antibody, IgE antibody, binding) True: (IgG antibody, IgE antibody, inhibit) PPI Misclassification examples Type3: Incorrect protein involved Sentence: AChE exhibits a butyrylcholinesterase (BuChE) activity that represents about 14% of AChE activity. False: (AChE, AChE, exhibits) True: (AChE, BuChE, exhibits ) PPI Possible Improvement syntactic patterns: “Optimizing syntax-patterns for discovering protein-protein interactions” In Proc ACM Symposium on Applied Computing, SAC, Bioinformatics Track, parse tree dependency parsing … The End