Argument-predicate distance as a filter for enhancing precision in extracting predications on the genetic etiology of disease

Masseroli, Marco; Kilicoglu, Halil; Lang, F.M.; Rindflesch, Thomas C.

doi:10.1186/1471-2105-7-291

Cited by 26 publications

(10 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although SemRep has been shown to perform well with pharmacogenomic relations [33], previous investigation has shown some indication that SemRep precision in predications related to genes can be improved by using only predications with low argument distance [44]. Argument distance is defined as the number of noun phrases between the predicate and the noun phrase selected as the argument of the predicate (subject or object).…”

Section: Discussionmentioning

confidence: 99%

Using semantic predications to uncover drug–drug interactions in clinical data

Zhang

Cairelli

Fiszman

et al. 2014

Journal of Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

In this study we report on potential drug-drug interactions between drugs occurring in patient clinical data. Results are based on relationships in SemMedDB, a database of structured knowledge extracted from all MEDLINE citations (titles and abstracts) using SemRep. The core of our methodology is to construct two potential drug-drug interaction schemas, based on relationships extracted from SemMedDB. In the first schema, Drug1 and Drug2 interact through Drug1’s effect on some gene, which in turn affects Drug2. In the second, Drug1 affects Gene1, while Drug2 affects Gene2. Gene1 and Gene2, together, then have an effect on some biological function. After checking each drug pair from the medication lists of each of 22 patients, we found 19 known and 62 unknown drug-drug interactions using both schemas. For example, our results suggest that the interaction of Lisinopril, an ACE inhibitor commonly prescribed for hypertension, and the antidepressant sertraline can potentially increase the likelihood and possibly the severity of psoriasis. We also assessed the relationships extracted by SemRep from a linguistic perspective and found that the precision of SemRep was 0.58 for 300 randomly selected sentences from MEDLINE. Our study demonstrates that the use of structured knowledge in the form of relationships from the biomedical literature can support the discovery of potential drug-drug interactions occurring in patient clinical data. Moreover, SemMedDB provides a good knowledge resource for expanding the range of drugs, genes, and biological functions considered as elements in various drug-drug interaction pathways.

show abstract

Section: Discussionmentioning

confidence: 99%

Using semantic predications to uncover drug–drug interactions in clinical data

Zhang

Cairelli

Fiszman

et al. 2014

Journal of Biomedical Informatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…The highlighted sentences are listed in ascending order of argument-predicate distance, which is measured as the number of noun phrases between the arguments (subject and object) and the word indicating the semantic relation (the predicate). In this regard, research by Masseroli et al [ 32 ] has shown that relations with lower argument-predicate distance have a higher likelihood of being correct. Therefore, we first show the user the relation instances that are more likely to be correct.…”

Section: Methodsmentioning

confidence: 99%

Biomedical question answering using semantic relations

et al. 2015

Self Cite

View full text Add to dashboard Cite

BackgroundThe proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature.ResultsWe extracted semantic relations with the SemRep natural language processing system from 122,421,765 sentences, which came from 21,014,382 MEDLINE citations (i.e., the complete MEDLINE distribution up to the end of 2012). A total of 58,879,300 semantic relation instances were extracted and organized in a relational database. The QA process is implemented as a search in this database, which is accessed through a Web-based application, called SemBT (available at http://sembt.mf.uni-lj.si). We conducted an extensive evaluation of the proposed methodology in order to estimate the accuracy of extracting a particular semantic relation from a particular sentence. Evaluation was performed by 80 domain experts. In total 7,510 semantic relation instances belonging to 2,675 distinct relations were evaluated 12,083 times. The instances were evaluated as correct 8,228 times (68%).ConclusionsIn this work we propose an innovative methodology for biomedical QA. The system is implemented as a Web-based application that is able to provide precise answers to a wide range of questions. A typical question is answered within a few seconds. The tool has some extensions that make it especially useful for interpretation of DNA microarray results.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0365-3) contains supplementary material, which is available to authorized users.

show abstract

“…It has been shown that smaller argument distance leads to higher precision in extracting interactions [62]. In the example below, both Bax and pro-caspase-3 are potential objects for p53 activity.…”

Section: Methodsmentioning

confidence: 95%

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

et al. 2014

Self Cite

View full text Add to dashboard Cite

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.

show abstract

Argument-predicate distance as a filter for enhancing precision in extracting predications on the genetic etiology of disease

Cited by 26 publications

References 41 publications

Using semantic predications to uncover drug–drug interactions in clinical data

Using semantic predications to uncover drug–drug interactions in clinical data

Biomedical question answering using semantic relations

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

Contact Info

Product

Resources

About