bio-medical interaction extractor syed toufeeq ahmed asu
TRANSCRIPT
Scopes
Various syntactic roles (such as Subject , Object and Modifying phrase) and their linguistically significant combinations makes up SCOPES
A SCOPE MATCHING is: Elementary (E) : If the scope contains a Gene /Protein (G)
name or an interaction word (I). Partial (P) : If the scope has a Gene/Protein (G) name and
an interaction word (I). Complete (C) : If the scope has at least two Gene /Protein
(G) names and an interaction word (I).
Scopes
Elementary(Subject) Elementary
(Object)
Partial(Modifying Phrase)
“HMBA could inhibit the MEC-1 cell proliferation by down-regulation of PCNA expression.”
Interaction(Verb)
Scopes & Matches
“The kinase phosphorylation of Gene1 by Gene2 could inhibit Gene3. ”
Complete(Subject)
Algorithm of Interaction Extractor:
S O MP
S-O S-M
Subject Modifying Phrase
Object
complete (G,I,G) interact: {G,I,G}
complete (G,I,G) interact: {G,I,G}
complete (G,I,G) interact: {G,I,G}
Elementary (G1) Elementary (G2)
Is Main Verb
an Interaction (I)
?
Interaction : { G1, I, G2 }
Partial (I,G2)
Interaction : { G1, I, G2 }
Example
Elementary(G)
Elementary(G)
Partial
“HMBA could inhibit the MEC-1 cell proliferation by down-regulation of PCNA expression.”
Main Verb(I)
{ “HMBA”, “inhibit”, “the MEC-1 cell proliferation” }
{ “HMBA”, “down-regulation”, “PCNA expression”}
Next Steps Handling negations in the sentences (such as “not
interact”, “fails to induce”, “does not inhibit”). Extraction of detailed contextual attributes of
interactions (such as bio-chemical context or location) by interpreting modifiers: Location/Position modifiers (in, at, on, into, up, over…) Agent/Accompaniment modifiers (by, with…) Purpose modifiers( for…) Theme/association modifiers ( of..)
Extraction of relationships between interactions from among multiple sentences in abstracts
(signaling pathways)
Preliminary Results
Dataset Precision % Recall %
Curated text 95.4 % 95.4 %
Abstracts 91.66 % 89.18 %