Evidence map›Paper›PMID 33737208›Full record

ArticleJournal of biomedical informatics2021

Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive.

Mike Wong, Paul Previde, Jack Cole, Brook Thomas, Nayana Laxmeshwar, Emily Mallory, Jake Lever, Dragutin Petkovic, Russ B Altman, Anagha Kulkarni

Abstract read
In one paragraph

Article in Journal of biomedical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Mike WongCOSE Computing for Life Sciences, San Francisco State University, San Francisco, CA, United States.
Paul PrevideDepartment of Computer Science, San Francisco State University, San Francisco, CA, United States.
Jack ColeDepartment of Computer Science, San Francisco State University, San Francisco, CA, United States.
Brook ThomasDepartment of Computer Science, San Francisco State University, San Francisco, CA, United States.
Nayana LaxmeshwarDepartment of Computer Science, San Francisco State University, San Francisco, CA, United States.
Emily MalloryBiomedical Informatics Training Program, Stanford University, Palo Alto, CA, United States.
Jake LeverPostdoctoral Scholar, Stanford University, Palo Alto, CA, United States.
Dragutin PetkovicDepartment of Computer Science, San Francisco State University, San Francisco, CA, United States; COSE Computing for Life Sciences, San Francisco State University, San Francisco, CA, United States.
Russ B AltmanDepartment of Bioengineering, Department of Genetics, and School of Medicine, Stanford University, Palo Alto, CA, United States.
Anagha KulkarniDepartment of Computer Science, San Francisco State University, San Francisco, CA, United States. Electronic address: ak@sfsu.edu.

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Text Mining for High-fidelity Curation and Discovery of Gene-drug-phenotype RelationshipsR01LM005652 · NLM · STANFORD UNIVERSITY · PI ALTMAN, RUSS BIAGIO · 2000 to 2018
$6.2M
NLM NIH HHS R01 LM005652
6 · The paper itself

Abstract

backgroundUnderstanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed. APPROACH: We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing.

resultsGeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases.

conclusionGeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.

Indexed as

Pharmaceutical PreparationsPrecision MedicineData MiningHumansPharmacogeneticsSoftwarePharmaceutical PreparationsBiomedical information retrievalGene-disease and gene-drug relationshipsGene interactionsGene setsRetrieval and visualization

Identifiers

PMID33737208
PMCPMC9042200

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.