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A bio text mining workbench combines natural language processing tools and machine learning methods to extract biological information from texts. It includes tools for named entity recognition of genes, proteins and other entities. It also extracts biological events involving interactions between entities. An active learning approach is used to improve the named entity recognition by selecting the most informative texts for human annotation. This helps enhance the model with less human effort. The workbench provides interfaces for corpus management, annotation and evaluation of the named entity and event extraction systems.

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A Bio Text Mining Workbench combined with Active Machine Learning Gary Geunbae Lee Postech 11/25 LBM2005
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object]
Introduction ,[object Object],[object Object],[object Object],Biological Papers ,[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/W : A development W orkbench ,[object Object]
POSBIOTM/W Workbench ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/W Workbench ,[object Object],[object Object]
POSBIOTM/W Workbench ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/W Workbench ,[object Object],[object Object]
POSBIOTM/W Workbench ,[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/W Workbench ,[object Object],[object Object],<Result> <NER> .... <Sentence SNum = &quot;4&quot;><protein>EDG-1</protein>, encoded by the <gene>endothelial_differentiation_gene-1</gene> , is a <protein>heterotrimeric_guanine_nucleotide_binding_protein-coupled_receptor</protein> ( <protein >GPCR</ protein > ) for < small_molecule >sphingosine-1-phosphate</ small_molecule > ( < small_molecule >SPP</ small_molecule > ) that has been shown to stimulate < cellular_process >angiogenesis</ cellular_process > and < cellular_process >cell_migration</ cellular_process > in cultured endothelial cells. </Sentence> ..... </NER> <Event_Extraction> <Event SNum = &quot;4&quot;> <Interaction>stimulate</Interaction> <Effecter>sphingosine-1-phosphate</Effecter> <Reactant>angiogenesis</Reactant> </Event> ..... </ Event_Extraction > </Result>
POSBIOTM/W Workbench ,[object Object],[object Object]
POSBIOTM/W Workbench ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/W Workbench ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/W Workbench ,[object Object]
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/NER System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/NER System ,[object Object],[object Object],base noun phrase tag of the previous/current/next words. Base noun phrase tag POS tag of the previous/current/next words. The part of speech is the term used to describe how a particular word is used. E.g. nouns, verb, etc. part-of-speech tag Prefixes/suffixes which are contained in the prefix/suffix dictionary. Biological prefix, suffix concept – ase, blast, cyt, phore, plast. prefix/suffix orthographical feature of the previous/current/next words. Upper case letters, numbers, non-alphabet letters. Greek words – alpha cells, beta hemolysis, tau interferon. word feature only in the case that the previous/current/next words are in the surface word dictionary. Lexical word Description Feature
POSBIOTM/NER System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/NER System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/NER System ,[object Object],[object Object],0.7 9 82 0.8 4 04 0. 75 50 GENE-NER 0.7370 0.8135 0.6736 GPCR-NER 0.6945 0.6929 0.6960 GENIA-NER F-Measure Recall Precision Corpus
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/NER with Active Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/NER with Active Learning ,[object Object]
POSBIOTM/NER with Active Learning ,[object Object],[object Object],[object Object],[object Object]
POSBIOTM/NER with Active Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],( for syntactic path)
POSBIOTM/NER with Active Learning ,[object Object],[object Object],[object Object]
POSBIOTM/NER with Active Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/NER with Active Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/NER with Active Learning ,[object Object]
POSBIOTM/NER with Active Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/Event System ,[object Object]
POSBIOTM/Event System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/Event System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],The cross-talk between PDGF and SPP is required for these embryonic cell movements .
POSBIOTM/Event System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/Event System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/Event System ,[object Object],[object Object]
POSBIOTM/Event System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/Event System ,[object Object],[object Object],4. Learn the long-span rule with the re-annotated sentence {NP} <E>cross-talk_between_PDGF_and_SPP</E> {/NP} {VP} is <BI>required</BI> {/VP} for {NP} these embryonic <CP>cell_movements</CP> {/NP} <TAGS> B {interaction require} {effecter cross-talk} {reactant cell movement} 1. Marking long NP boundary 2. Learn the short-span rule corresponding to the NP: “<BI>cross-talk</BI> between <P>PDGF</P> and <SM>SPP</SM>”  “ {NP} (<BI>)[I] between (<P>)[E] and (<SM>)[R] {/NP} “ 3. Re-annotate the short-span interaction as one noun with regular expression format {NP} <BI>cross-talk</BI> between <P>PDGF</P> and <SM>SPP</SM> {/NP} {VP} is <BI>required</BI> {/VP} for {NP} these embryonic <CP>cell_movements</CP> {/NP} <TAGS> B {interaction cross-talk} {effecter PDGF} {reactant SPP} <TAGS> B {interaction require} {effecter cross-talk} {reactant cell movement}
POSBIOTM/Event System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/Event System ,[object Object]
POSBIOTM/Event System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/Event System ,[object Object]
POSBIOTM/Event System ,[object Object],[object Object],Verified Biological Extracted Events Ev1: Requires (I) sphingosine_kinase (E) cell_migration (R) Ev2: Requires (I) EDG-1 (E) cell_migration (R) Event Component Verifier Results I : Requires E : EDG-1, sphingosine_kinase, PDGF R : cell_migration Extracted Biological Events Ev1: Requires (I) sphingosine_kinase(E) cell_migration (R) Ev2: Requires (I) EDG-1 (E) cell_migration (R) Ev3: Requires (I) EDG-1 (E) PDGF (R)
POSBIOTM/Event System ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],46.1 58.0 38.3 Before verification Flat rule learner 51.8 49.2 54.7 After verification 48.2 54.6 48.9 F-measure 63 56.1 68.0 Recall(%) 39 53.1 38.2 Precision(%) After verification Before verification Comparison system Two-level rule learner
POSBIOTM/Event System ,[object Object],[object Object],[object Object],[object Object],[object Object]
POSBIOTM/Event System ,[object Object],[object Object]
Other Corpora for Bio-Relation Extraction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Current Status & future works ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Workbench Demo

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Presentation material

  • 1. A Bio Text Mining Workbench combined with Active Machine Learning Gary Geunbae Lee Postech 11/25 LBM2005
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