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Getting Started with the IDG KMC Datasets and Tools.
Kropiwnicki, Eryk; Binder, Jessica L; Yang, Jeremy J; Holmes, Jayme; Lachmann, Alexander; Clarke, Daniel J B; Sheils, Timothy; Kelleher, Keith J; Metzger, Vincent T; Bologa, Cristian G; Oprea, Tudor I; Ma'ayan, Avi.
  • Kropiwnicki E; Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Binder JL; Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
  • Yang JJ; Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
  • Holmes J; Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
  • Lachmann A; Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Clarke DJB; Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
  • Sheils T; National Center for Advancing Translational Sciences, Rockville, Maryland.
  • Kelleher KJ; National Center for Advancing Translational Sciences, Rockville, Maryland.
  • Metzger VT; Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
  • Bologa CG; Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
  • Oprea TI; Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
  • Ma'ayan A; Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, New York.
Curr Protoc ; 2(1): e355, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: covidwho-1653213
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.
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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Genoma / Bases de Datos Genéticas Tipo de estudio: Estudio pronóstico / Revisiones Límite: Humanos Idioma: Inglés Revista: Curr Protoc Año: 2022 Tipo del documento: Artículo

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Genoma / Bases de Datos Genéticas Tipo de estudio: Estudio pronóstico / Revisiones Límite: Humanos Idioma: Inglés Revista: Curr Protoc Año: 2022 Tipo del documento: Artículo