Getting started in text mining.
Ontology highlight
ABSTRACT:
SUBMITTER: Cohen KB
PROVIDER: S-EPMC2217579 | biostudies-literature | 2008 Jan
REPOSITORIES: biostudies-literature
Publications
Getting started in text mining.
Cohen K Bretonnel KB Hunter Lawrence L
PLoS computational biology 20080101 1
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Project description: Not available
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Project description:The Library of Integrated Network-based Cellular Signatures (LINCS) was an NIH Common Fund program that aimed to expand our knowledge about human cellular responses to chemical, genetic, and microenvironment perturbations. Responses to perturbations were measured by transcriptomics, proteomics, cellular imaging, and other high content assays. The second phase of the LINCS program, which lasted 7 years, involved the engagement of six data and signature generation centers (DSGCs) and one data coordination and integration center (DCIC). The DSGCs and the DCIC developed several digital resources, including tools, databases, and workflows that aim to facilitate the use of the LINCS data and integrate this data with other publicly available data. The digital resources developed by the DSGCs and the DCIC can be used to gain new biological and pharmacological insights that can lead to the development of novel therapeutics. This protocol provides step-by-step instructions for processing the LINCS data into signatures, and utilizing the digital resources developed by the LINCS consortia for hypothesis generation and knowledge discovery. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Navigating L1000 tools and data in CLUE.io Basic Protocol 2: Computing signatures from the L1000 data with the CD method Basic Protocol 3: Analyzing lists of differentially expressed genes and querying them against the L1000 data with BioJupies and the Bulk RNA-seq Appyter Basic Protocol 4: Utilizing the L1000FWD resource for drug discovery Basic Protocol 5: KINOMEscan and the KINOMEscan Appyter Basic Protocol 6: LINCS P100 and GCP Proteomics Assays Basic Protocol 7: The LINCS Joint Project (LJP) Basic Protocol 8: The LINCS Data Portals and SigCom LINCS Basic Protocol 9: Creating and analyzing signatures with iLINCS.
| S-EPMC9326873 | biostudies-literature
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| S-EPMC2323403 | biostudies-literature
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| S-EPMC2845645 | biostudies-literature
Project description: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.
| S-EPMC10789444 | biostudies-literature
Project description:Understanding basic concepts of electronics and computer programming allows researchers to get the most out of the equipment found in their laboratories. Although a number of platforms have been specifically designed for the general public and are supported by a vast array of on-line tutorials, this subject is not normally included in university chemistry curricula. Aiming to provide the basic concepts of hardware and software, this article is focused on the design and use of a simple module to control a series of PDMS-based valves. The module is based on a low-cost microprocessor (Teensy) and open-source software (Arduino). The microvalves were fabricated using thin sheets of PDMS and patterned using CO2 laser engraving, providing a simple and efficient way to fabricate devices without the traditional photolithographic process or facilities. Synchronization of valve control enabled the development of two simple devices to perform injection (1.6 ± 0.4 μL/stroke) and mixing of different solutions. Furthermore, a practical demonstration of the utility of this system for microscale chemical sample handling and analysis was achieved performing an on-chip acid-base titration, followed by conductivity detection with an open-source low-cost detection system. Overall, the system provided a very reproducible (98%) platform to perform fluid delivery at the microfluidic scale.
| S-EPMC4176689 | biostudies-literature
Project description:This tutorial introduces the graded response model (GRM), a tool for testing measurement precision within the item response theory (IRT) paradigm, which is useful for informing researchers about the item and person properties of their measurement. The tutorial aims to guide applied researchers through a unidimensional GRM analysis in the R environment, using the psych, mirt and ggmirt packages. GRM is specifically designed to examine the psychometric properties of psychological scales with polytomous (Likert-style) items. The tutorial illustrates the procedure using data from the Open Psychometrics Database on the right-wing authoritarianism (RWA) scale, outlining the theoretical underpinnings of GRM and steps for data preparation, testing model assumptions, model fitting, plotting item parameters and interpretation of results.
| S-EPMC11626089 | biostudies-literature
Project description:Research in Pediatric Hospital Medicine is growing and expanding rapidly, and with this comes the need to expand single-site research projects into multisite research studies within practice-based research networks. This expansion is crucial to ensure generalizable findings in diverse populations; however, expanding Pediatric Hospital Medicine research projects from single to multisite can be daunting. We provide an overview of major logistical steps and challenges in project management, regulatory approvals, data use agreements, training, communication, and financial management that are germane to hospitalist researchers launching their first multisite project by sharing processes and lessons learned from running multisite research projects in the Pediatric Research in Inpatient Settings Network within the Eliminating Monitor Overuse study portfolio. This description is relevant to hospitalist researchers transitioning from single-site to multisite research or those considering serving as site lead for a multisite project.
| S-EPMC10896739 | biostudies-literature
Project description:BackgroundDecoding transcriptional regulatory networks and the genomic cis-regulatory logic implemented in their control nodes is a fundamental challenge in genome biology. High-throughput computational and experimental analyses of regulatory networks and sequences rely heavily on positive control data from prior small-scale experiments, but the vast majority of previously discovered regulatory data remains locked in the biomedical literature.ResultsWe develop text-mining strategies to identify relevant publications and extract sequence information to assist the regulatory annotation process. Using a vector space model to identify Medline abstracts from papers likely to have high cis-regulatory content, we demonstrate that document relevance ranking can assist the curation of transcriptional regulatory networks and estimate that, minimally, 30,000 papers harbor unannotated cis-regulatory data. In addition, we show that DNA sequences can be extracted from primary text with high cis-regulatory content and mapped to genome sequences as a means of identifying the location, organism and target gene information that is critical to the cis-regulatory annotation process.ConclusionOur results demonstrate that text-mining technologies can be successfully integrated with genome annotation systems, thereby increasing the availability of annotated cis-regulatory data needed to catalyze advances in the field of gene regulation.
| S-EPMC2374703 | biostudies-literature