Retrieval with gene queries

Sehgal, Aditya Kumar; Srinivasan, Padmini

doi:10.1186/1471-2105-7-220

Cited by 17 publications

(6 citation statements)

References 21 publications

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“…Ambiguity associated with gene names : Ambiguity of gene names—symbols, aliases and other designations—adds significant error to estimates of literature coverage derived from PubMed [8] . We used a simple disambiguation approach that combines ambiguous gene names with informative and gene-specific key words (Methods).…”

Section: Resultsmentioning

confidence: 99%

Functionally Enigmatic Genes: A Case Study of the Brain Ignorome

Pandey

Wang

et al. 2014

PLoS ONE

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What proportion of genes with intense and selective expression in specific tissues, cells, or systems are still almost completely uncharacterized with respect to biological function? In what ways do these functionally enigmatic genes differ from well-studied genes? To address these two questions, we devised a computational approach that defines so-called ignoromes. As proof of principle, we extracted and analyzed a large subset of genes with intense and selective expression in brain. We find that publications associated with this set are highly skewed—the top 5% of genes absorb 70% of the relevant literature. In contrast, approximately 20% of genes have essentially no neuroscience literature. Analysis of the ignorome over the past decade demonstrates that it is stubbornly persistent, and the rapid expansion of the neuroscience literature has not had the expected effect on numbers of these genes. Surprisingly, ignorome genes do not differ from well-studied genes in terms of connectivity in coexpression networks. Nor do they differ with respect to numbers of orthologs, paralogs, or protein domains. The major distinguishing characteristic between these sets of genes is date of discovery, early discovery being associated with greater research momentum—a genomic bandwagon effect. Finally we ask to what extent massive genomic, imaging, and phenotype data sets can be used to provide high-throughput functional annotation for an entire ignorome. In a majority of cases we have been able to extract and add significant information for these neglected genes. In several cases—ELMOD1, TMEM88B, and DZANK1—we have exploited sequence polymorphisms, large phenome data sets, and reverse genetic methods to evaluate the function of ignorome genes.

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Section: Resultsmentioning

confidence: 99%

Functionally Enigmatic Genes: A Case Study of the Brain Ignorome

Pandey

Wang

et al. 2014

PLoS ONE

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“…Because of ambiguities in gene names and symbols we filter the retrieved documents. Most of the logic for gene name disambiguation is as used in our earlier research [ 30 ]. Our goal is to make sure that the retrieved record is about a gene.…”

Section: Methodsmentioning

confidence: 99%

Ferret: a sentence-based literature scanning system

et al. 2015

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BackgroundThe rapid pace of bioscience research makes it very challenging to track relevant articles in one’s area of interest. MEDLINE, a primary source for biomedical literature, offers access to more than 20 million citations with three-quarters of a million new ones added each year. Thus it is not surprising to see active research in building new document retrieval and sentence retrieval systems. We present Ferret, a prototype retrieval system, designed to retrieve and rank sentences (and their documents) conveying gene-centric relationships of interest to a scientist. The prototype has several features. For example, it is designed to handle gene name ambiguity and perform query expansion. Inputs can be a list of genes with an optional list of keywords. Sentences are retrieved across species but the species discussed in the records are identified. Results are presented in the form of a heat map and sentences corresponding to specific cells of the heat map may be selected for display. Ferret is designed to assist bio scientists at different stages of research from early idea exploration to advanced analysis of results from bench experiments.ResultsThree live case studies in the field of plant biology are presented related to Arabidopsis thaliana. The first is to discover genes that may relate to the phenotype of open immature flower in Arabidopsis. The second case is about finding associations reported between ethylene signaling and a set of 300+ Arabidopsis genes. The third case is on searching for potential gene targets of an Arabidopsis transcription factor hypothesized to be involved in plant stress responses. Ferret was successful in finding valuable information in all three cases. In the first case the bZIP family of genes was identified. In the second case sentences indicating relevant associations were found in other species such as potato and jasmine. In the third sentences led to new research questions about the plant hormone salicylic acid.ConclusionsFerret successfully retrieved relevant gene-centric sentences from PubMed records. The three case studies demonstrate end user satisfaction with the system.

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“…Since data mining is not the focus of this work, we give some further pointers to readers who might be more interested in this area. Works on data mining in the clinical domain include [ 20 - 25 ]. Further, there has been recent work on the use of medical ontologies to improve the search quality, where the ontology is generally used to perform query expansion [ 26 - 28 ].…”

Section: Methodsmentioning

confidence: 99%

Information Discovery on Electronic Health Records Using Authority Flow Techniques

Hristidis

Varadarajan

Biondich

et al. 2010

BMC Med Inform Decis Mak

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BackgroundAs the use of electronic health records (EHRs) becomes more widespread, so does the need to search and provide effective information discovery within them. Querying by keyword has emerged as one of the most effective paradigms for searching. Most work in this area is based on traditional Information Retrieval (IR) techniques, where each document is compared individually against the query. We compare the effectiveness of two fundamentally different techniques for keyword search of EHRs.MethodsWe built two ranking systems. The traditional BM25 system exploits the EHRs' content without regard to association among entities within. The Clinical ObjectRank (CO) system exploits the entities' associations in EHRs using an authority-flow algorithm to discover the most relevant entities. BM25 and CO were deployed on an EHR dataset of the cardiovascular division of Miami Children's Hospital. Using sequences of keywords as queries, sensitivity and specificity were measured by two physicians for a set of 11 queries related to congenital cardiac disease.ResultsOur pilot evaluation showed that CO outperforms BM25 in terms of sensitivity (65% vs. 38%) by 71% on average, while maintaining the specificity (64% vs. 61%). The evaluation was done by two physicians.ConclusionsAuthority-flow techniques can greatly improve the detection of relevant information in EHRs and hence deserve further study.

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Retrieval with gene queries

Cited by 17 publications

References 21 publications

Functionally Enigmatic Genes: A Case Study of the Brain Ignorome

Functionally Enigmatic Genes: A Case Study of the Brain Ignorome

Ferret: a sentence-based literature scanning system

Information Discovery on Electronic Health Records Using Authority Flow Techniques

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