Information Extraction from Literature

1. Information Extraction from Literature Yue Lu BeeSpace Seminar Oct 24, 2007

2. Outline • Overview of BeeSpace v4 • Entity Recognition • Relation Extraction

3. 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

4. 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

5. 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

6. 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

7. Outline • Overview of BeeSpace v4 • Entity Recognition • Relation Extraction

8. Entity Recognition • Gene • Example: • Although <GENE>mxp</GENE> and <GENE>Pb</GENE> display very similar expression patterns, <GENE>pb</GENE> null embryos develop normally

9. 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.

10. 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>.

11. 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 proto-oncogenes that mediate cellular responses in vertebrates including humans

12. 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.

13. 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).

14. Entity Recognition • Phenotype • Definition: • a set of observable physical characteristics of an individual organism • Example: • Fog, dumpy

15. 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

16. 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:?

17. 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

18. 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

20. Entity Recognition Resources:

21. Outline • Overview of BeeSpace v4 • Entity Recognition • Relation Extraction

22. Relation Extraction • Expression Location • the expression of a gene in some location (tissues, body parts) • Homology/Orthology • one gene is homologous to another gene

23. 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)

24. Relation Extraction • Generally can be defined as a classification problem, which requires training data • Domain adaptation? • an example of PPI

25. 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

26. 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

27. 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

28. PPI • Training data: • BioCreative • 1000 hand labeled sentences, 3964 tuples • Testing Data (different domain) • Bee collection • Performance (Judged by Moushumi) • Total number of tuples extracted as PPI instances: 92 • Precision: 63%

29. 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

30. 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)

31. PPI Misclassification examples • Type3: Incorrect protein involved • Sentence: AChEexhibits a butyrylcholinesterase (BuChE) activity that represents about 14% of AChE activity. • False: (AChE, AChE, exhibits) • True: (AChE, BuChE, exhibits )

32. 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 • …

33. The End