Evidence map›Paper›PMID 35085427›Full record

ArticleCurrent protocols2022

Getting Started with the IDG KMC Datasets and Tools.

Eryk Kropiwnicki, Jessica L Binder, Jeremy J Yang, Jayme Holmes, Alexander Lachmann, Daniel J B Clarke, Timothy Sheils, Keith J Kelleher, Vincent T Metzger, Cristian G Bologa and 2 more

Abstract read
In one paragraph

Article in Current protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

12 authors.

Eryk KropiwnickiDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Jessica L BinderTranslational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
Jeremy J YangTranslational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
Jayme HolmesTranslational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
Alexander LachmannDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Daniel J B ClarkeDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Timothy SheilsNational Center for Advancing Translational Sciences, Rockville, Maryland.
Keith J KelleherNational Center for Advancing Translational Sciences, Rockville, Maryland.
Vincent T MetzgerTranslational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
Cristian G BologaTranslational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
Tudor I OpreaTranslational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
Avi Ma'ayanDepartment of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.

Funding

Data Coordination and Integration Center for LINCS-BD2KU54HL127624 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MA'AYAN, AVI, SCHURER, STEPHAN C · 2014 to 2019
$23.5M
Machine Learning Model Validation for AD/ADRDU24CA224370 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI EDWARDS, JEREMY S · 2018 to 2023
$6.4M
Resource Dissemination and Outreach Center for Illuminating the Druggable GenomeU24TR002278 · NCATS · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · PI SCHURER, STEPHAN C, SKLAR, LARRY A. · 2018 to 2023
$3.7M
Illuminating the Druggable Genome Data Coordinating Center - Engagement Plan with the CFDEOT2OD030546 · OD · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI LAMBERT, CHRISTOPHE G., YANG, JEREMY JOSEPH · 2020 to 2025
$2.8M
Illuminating the Druggable Genome by Knowledge GraphsU01CA239108 · NCI · JACKSON LABORATORY · PI MUNGALL, CHRISTOPHER J, OPREA, TUDOR I · 2019 to 2021
$1.7M
Knowledge Management Center for Illuminating the Druggable GenomeU24CA224260 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MA'AYAN, AVI · 2018 to 2023
$1.5M
NCATS NIH HHS U24 TR002278NCI NIH HHS U01 CA239108NCI NIH HHS U24 CA224260NCI NIH HHS U24 CA224370NHLBI NIH HHS U54 HL127624NIH HHS OT2 OD030546NIH HHS OT2OD030546NIH HHS U01CA239108NIH HHS U24CA224260NIH HHS U24CA224370NIH HHS U24TR002278NIH HHS U54HL127624
6 · The paper itself

Abstract

The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.

Indexed as

Databases, GeneticGenomeCOVID-19HumansMachine LearningProteinsSARS-CoV-2Proteinsbioinformaticsdata visualizationdisease ontologydrug discoverydruggable genomedrug targetsweb applications

Identifiers

PMID35085427
PMCPMC10789444

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.