Dataset Information

Geneshot: search engine for ranking genes from arbitrary text queries.

ABSTRACT: The frequency by which genes are studied correlates with the prior knowledge accumulated about them. This leads to an imbalance in research attention where some genes are highly investigated while others are ignored. Geneshot is a search engine developed to illuminate this gap and to promote attention to the under-studied genome. Through a simple web interface, Geneshot enables researchers to enter arbitrary search terms, to receive ranked lists of genes relevant to the search terms. Returned ranked gene lists contain genes that were previously published in association with the search terms, as well as genes predicted to be associated with the terms based on data integration from multiple sources. The search results are presented with interactive visualizations. To predict gene function, Geneshot utilizes gene-gene similarity matrices from processed RNA-seq data, or from gene-gene co-occurrence data obtained from multiple sources. In addition, Geneshot can be used to analyze the novelty of gene sets and augment gene sets with additional relevant genes. The Geneshot web-server and API are freely and openly available from https://amp.pharm.mssm.edu/geneshot.

SUBMITTER: Lachmann A

PROVIDER: S-EPMC6602493 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Geneshot: search engine for ranking genes from arbitrary text queries.

Lachmann Alexander A Schilder Brian M BM Wojciechowicz Megan L ML Torre Denis D Kuleshov Maxim V MV Keenan Alexandra B AB Ma'ayan Avi A

Nucleic acids research 20190701 W1

The frequency by which genes are studied correlates with the prior knowledge accumulated about them. This leads to an imbalance in research attention where some genes are highly investigated while others are ignored. Geneshot is a search engine developed to illuminate this gap and to promote attention to the under-studied genome. Through a simple web interface, Geneshot enables researchers to enter arbitrary search terms, to receive ranked lists of genes relevant to the search terms. Returned ra ...[more]

PMID: 31114885

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Project description:BACKGROUND: Biologists may need to know the set of genes that are semantically related to a given set of genes. For instance, a biologist may need to know the set of genes related to another set of genes known to be involved in a specific disease. Some works use the concept of gene clustering in order to identify semantically related genes. Others propose tools that return the set of genes that are semantically related to a given set of genes. Most of these gene similarity measures determine the semantic similarities among the genes based solely on the proximity to each other of the GO terms annotating the genes, while overlook the structural dependencies among these GO terms, which may lead to low recall and precision of results. RESULTS: We propose in this paper a search engine called GRank, which overcomes the limitations of the current gene similarity measures outlined above as follows. It employs the concept of existence dependency to determine the structural dependencies among the GO terms annotating a given set of gene. After determining the set of genes that are semantically related to input genes, GRank would use microarray experiment to rank these genes based on their degree of relativity to the input genes. We evaluated GRank experimentally and compared it with a comparable gene prediction tool called DynGO, which retrieves the genes and gene products that are relatives of input genes. Results showed marked improvement. CONCLUSIONS: The experimental results demonstrated that GRank overcomes the limitations of current gene similarity measures. We attribute this performance to GRank's use of existence dependency concept for determining the semantic relationships among gene annotations. The recall and precision values for two benchmarking datasets showed that GRank outperforms DynGO tool, which does not employ the concept of existence dependency. The demo of GRank using 11000 KEGG yeast genes and a Gene Expression Omnibus (GEO) microarray file named "GSM34635.pad" is available at: http://ecesrvr.kustar.ac.ae:8080/ (click on the link labelled Gene Ontology 2).

Project description:BackgroundStroke is a major cause of death and the need for nursing care in Japan, with large regional disparities.ObjectiveThe purpose of this study was to clarify the association between stroke-related information retrieval behavior and age-adjusted mortality in each prefecture in Japan.MethodsAge-adjusted mortality from stroke and aging rates were obtained from publicly available Japanese government statistics. A total of 9476 abstracts of Japanese articles related to symptoms and signs of stroke were identified in Ichushi-Web, a Japanese web-based database of biomedical articles, and 100 highly frequent words (hereafter referred to as the Stroke 100) were extracted. Using data from 2014 to 2019, a random forest analysis was carried out using the age-adjusted mortality from stroke in 47 prefectures as the outcome variable and the standardized retrieval numbers of the Stroke 100 words in the log data of Yahoo! JAPAN Search as predictive variables. Regression analysis was performed using a generalized linear mixed model (GLMM) with the number of standardized searches for Stroke 100 words with high importance scores in the random forest model as the predictive variable. In the GLMM, the aging rate and data year were used as control variables, and the random slope of data year and random intercept were calculated by prefecture.ResultsThe mean age-adjusted mortality from stroke was 28.07 (SD 4.55) deaths per 100,000 for all prefectures in all data years. The accuracy score of the random forest analysis was 89.94%, the average error was 2.79 degrees, and the mean squared error was 13.57 degrees. The following 9 variables with high importance scores in the random forest analysis were selected as predictive variables for the regression analysis: male, age, hospitalization, enforcement, progress, stroke, abnormal, use, and change. As a result of the regression analysis with GLMM, the standardized partial regression coefficients (β) and 95% confidence intervals showed that the following internet search terms were significantly associated with age-adjusted mortality from stroke: male (β=-5.83, 95% CI -8.67 to -3.29), age (β=-5.83, 95% CI -8.67 to -3.29), hospitalization (β=-5.83, 95% CI -8.67 to -3.29), and abnormal (β=3.83, 95% CI 1.14 to 6.56).ConclusionsStroke-related search behavior was associated with age-adjusted mortality from stroke in each prefecture in Japan. Query terms that were strongly associated with age-adjusted mortality rates of stroke suggested the possibility that individual characteristics, such as sex and age, have an impact on stroke-associated mortality and that it is important to receive medical care early after stroke onset. Further studies on the criteria and timing of alerting are needed by monitoring information-seeking behavior to identify queries that are strongly associated with stroke mortality.

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Geneshot: search engine for ranking genes from arbitrary text queries.

Publications

Geneshot: search engine for ranking genes from arbitrary text queries.

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