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2016 ; 23
(4
): 758-65
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Automated identification of molecular effects of drugs (AIMED)
#MMPMID27107438
Fathiamini S
; Johnson AM
; Zeng J
; Araya A
; Holla V
; Bailey AM
; Litzenburger BC
; Sanchez NS
; Khotskaya Y
; Xu H
; Meric-Bernstam F
; Bernstam EV
; Cohen T
J Am Med Inform Assoc
2016[Jul]; 23
(4
): 758-65
PMID27107438
show ga
INTRODUCTION: Genomic profiling information is frequently available to
oncologists, enabling targeted cancer therapy. Because clinically relevant
information is rapidly emerging in the literature and elsewhere, there is a need
for informatics technologies to support targeted therapies. To this end, we have
developed a system for Automated Identification of Molecular Effects of Drugs, to
help biomedical scientists curate this literature to facilitate decision support.
OBJECTIVES: To create an automated system to identify assertions in the
literature concerning drugs targeting genes with therapeutic implications and
characterize the challenges inherent in automating this process in rapidly
evolving domains. METHODS: We used subject-predicate-object triples (semantic
predications) and co-occurrence relations generated by applying the SemRep
Natural Language Processing system to MEDLINE abstracts and ClinicalTrials.gov
descriptions. We applied customized semantic queries to find drugs targeting
genes of interest. The results were manually reviewed by a team of experts.
RESULTS: Compared to a manually curated set of relationships, recall, precision,
and F2 were 0.39, 0.21, and 0.33, respectively, which represents a 3- to 4-fold
improvement over a publically available set of predications (SemMedDB) alone.
Upon review of ostensibly false positive results, 26% were considered relevant
additions to the reference set, and an additional 61% were considered to be
relevant for review. Adding co-occurrence data improved results for drugs in
early development, but not their better-established counterparts. CONCLUSIONS:
Precision medicine poses unique challenges for biomedical informatics systems
that help domain experts find answers to their research questions. Further
research is required to improve the performance of such systems, particularly for
drugs in development.
|*Natural Language Processing
[MESH]
|*Precision Medicine
[MESH]
|Antineoplastic Agents/*pharmacology/therapeutic use
[MESH]
|Clinical Trials as Topic
[MESH]
|Humans
[MESH]
|Information Storage and Retrieval/*methods
[MESH]
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