Dataset Information

EGARD: Extracting associations between genomic anomalies and drug responses from text.

ABSTRACT: Tumor molecular profiling plays an integral role in identifying genomic anomalies which may help in personalizing cancer treatments, improving patient outcomes and minimizing risks associated with different therapies. However, critical information regarding the evidence of clinical utility of such anomalies is largely buried in biomedical literature. It is becoming prohibitive for biocurators, clinical researchers and oncologists to keep up with the rapidly growing volume and breadth of information, especially those that describe therapeutic implications of biomarkers and therefore relevant for treatment selection. In an effort to improve and speed up the process of manually reviewing and extracting relevant information from literature, we have developed a natural language processing (NLP)-based text mining (TM) system called eGARD (extracting Genomic Anomalies association with Response to Drugs). This system relies on the syntactic nature of sentences coupled with various textual features to extract relations between genomic anomalies and drug response from MEDLINE abstracts. Our system achieved high precision, recall and F-measure of up to 0.95, 0.86 and 0.90, respectively, on annotated evaluation datasets created in-house and obtained externally from PharmGKB. Additionally, the system extracted information that helps determine the confidence level of extraction to support prioritization of curation. Such a system will enable clinical researchers to explore the use of published markers to stratify patients upfront for 'best-fit' therapies and readily generate hypotheses for new clinical trials.

SUBMITTER: Mahmood ASMA

PROVIDER: S-EPMC5738129 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

eGARD: Extracting associations between genomic anomalies and drug responses from text.

Mahmood A S M Ashique ASMA Rao Shruti S McGarvey Peter P Wu Cathy C Madhavan Subha S Vijay-Shanker K K

PloS one 20171220 12

Tumor molecular profiling plays an integral role in identifying genomic anomalies which may help in personalizing cancer treatments, improving patient outcomes and minimizing risks associated with different therapies. However, critical information regarding the evidence of clinical utility of such anomalies is largely buried in biomedical literature. It is becoming prohibitive for biocurators, clinical researchers and oncologists to keep up with the rapidly growing volume and breadth of informat ...[more]

PMID: 29261751

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Project description:BackgroundEnzymatic and chemical reactions are key for understanding biological processes in cells. Curated databases of chemical reactions exist but these databases struggle to keep up with the exponential growth of the biomedical literature. Conventional text mining pipelines provide tools to automatically extract entities and relationships from the scientific literature, and partially replace expert curation, but such machine learning frameworks often require a large amount of labeled training data and thus lack scalability for both larger document corpora and new relationship types.ResultsWe developed an application of Snorkel, a weakly supervised learning framework, for extracting chemical reaction relationships from biomedical literature abstracts. For this work, we defined a chemical reaction relationship as the transformation of chemical A to chemical B. We built and evaluated our system on small annotated sets of chemical reaction relationships from two corpora: curated bacteria-related abstracts from the MetaCyc database (MetaCyc_Corpus) and a more general set of abstracts annotated with MeSH (Medical Subject Headings) term Bacteria (Bacteria_Corpus; a superset of MetaCyc_Corpus). For the MetaCyc_Corpus, we obtained 84% precision and 41% recall (55% F1 score). Extending to the more general Bacteria_Corpus decreased precision to 62% with only a four-point drop in recall to 37% (46% F1 score). Overall, the Bacteria_Corpus contained two orders of magnitude more candidate chemical reaction relationships (nine million candidates vs 68,0000 candidates) and had a larger class imbalance (2.5% positives vs 5% positives) as compared to the MetaCyc_Corpus. In total, we extracted 6871 chemical reaction relationships from nine million candidates in the Bacteria_Corpus.ConclusionsWith this work, we built a database of chemical reaction relationships from almost 900,000 scientific abstracts without a large training set of labeled annotations. Further, we showed the generalizability of our initial application built on MetaCyc documents enriched with chemical reactions to a general set of articles related to bacteria.

Project description:ObjectivesLifetime occupational history (OH) questionnaires often use open-ended questions to capture detailed information about study participants' jobs. Exposure assessors use this information, along with responses to job- and industry-specific questionnaires, to assign exposure estimates on a job-by-job basis. An alternative approach is to use information from the OH responses and the job- and industry-specific questionnaires to develop programmable decision rules for assigning exposures. As a first step in this process, we developed a systematic approach to extract the free-text OH responses and convert them into standardized variables that represented exposure scenarios.MethodsOur study population comprised 2408 subjects, reporting 11991 jobs, from a case-control study of renal cell carcinoma. Each subject completed a lifetime OH questionnaire that included verbatim responses, for each job, to open-ended questions including job title, main tasks and activities (task), tools and equipment used (tools), and chemicals and materials handled (chemicals). Based on a review of the literature, we identified exposure scenarios (occupations, industries, tasks/tools/chemicals) expected to involve possible exposure to chlorinated solvents, trichloroethylene (TCE) in particular, lead, and cadmium. We then used a SAS macro to review the information reported by study participants to identify jobs associated with each exposure scenario; this was done using previously coded standardized occupation and industry classification codes, and a priori lists of associated key words and phrases related to possibly exposed tasks, tools, and chemicals. Exposure variables representing the occupation, industry, and task/tool/chemicals exposure scenarios were added to the work history records of the study respondents. Our identification of possibly TCE-exposed scenarios in the OH responses was compared to an expert's independently assigned probability ratings to evaluate whether we missed identifying possibly exposed jobs.ResultsOur process added exposure variables for 52 occupation groups, 43 industry groups, and 46 task/tool/chemical scenarios to the data set of OH responses. Across all four agents, we identified possibly exposed task/tool/chemical exposure scenarios in 44-51% of the jobs in possibly exposed occupations. Possibly exposed task/tool/chemical exposure scenarios were found in a nontrivial 9-14% of the jobs not in possibly exposed occupations, suggesting that our process identified important information that would not be captured using occupation alone. Our extraction process was sensitive: for jobs where our extraction of OH responses identified no exposure scenarios and for which the sole source of information was the OH responses, only 0.1% were assessed as possibly exposed to TCE by the expert.ConclusionsOur systematic extraction of OH information found useful information in the task/chemicals/tools responses that was relatively easy to extract and that was not available from the occupational or industry information. The extracted variables can be used as inputs in the development of decision rules, especially for jobs where no additional information, such as job- and industry-specific questionnaires, is available.

Project description:ObjectiveThe coronavirus disease 2019 (COVID-19) health crisis that began at the end of 2019 made researchers around the world quickly race to find effective solutions. Related literature exploded and it was inevitable that an automated approach was needed to find useful information, namely text mining, to overcome COVID-19, especially in terms of drug candidate discovery. While text mining methods for finding drug candidates mostly try to extract bioentity associations from PubMed, very few of them mine with a clustering approach. The purpose of this study was to demonstrate the effectiveness of our approach to identify drugs for the prevention of COVID-19 through literature review, cluster analysis, drug docking calculations, and clinical trial data.MethodsThis research was conducted in four main stages. First, the text mining stage was carried out by involving Bidirectional Encoder Representations from Transformers for Biomedical to obtain vector representation of each word in the sentence from texts. The next stage generated the disease-drug associations, which were obtained from the correlation between disease and drug. Next, the clustering stage grouped the rules through the similarity of diseases by utilizing Term Frequency-Inverse Document Frequency as its feature. Finally, the drug candidate extraction stage was processed through leveraging PubChem and DrugBank databases. We further used the drug docking package AUTODOCK VINA in PyRx software to verify the results.ResultsComparative analyses showed that the percentage of findings using mining with clustering outperformed mining without clustering in all experimental settings. In addition, we suggest that the top three drugs/phytochemicals by drug docking analysis may be effective in preventing COVID-19.ConclusionsThe proposed method for text mining utilizing the clustering method is quite promising in the discovery of drug candidates for the prevention of COVID-19 through the biomedical literature.

Dataset Information

EGARD: Extracting associations between genomic anomalies and drug responses from text.

Publications

eGARD: Extracting associations between genomic anomalies and drug responses from text.

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