ArticlePLoS computational biology2012
Chapter 1: Biomedical knowledge integration.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
The trial behind it
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Who cites it
11 citing papers in PubMed.
- RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine.BMC bioinformatics · 2022Article
- BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials.Medical & biological engineering & computing · 2020Article
- Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.PLoS computational biology · 2019Review
- Precision medicine review: rare driver mutations and their biophysical classification.Biophysical reviews · 2019Review
- Development of an informatics system for accelerating biomedical research.F1000Research · 2019Article
- Validation of the Behavior of a Knowledge Base Implementing Clinical Guidelines for Point-of-Care Antiretroviral Toxicity Monitoring.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- MinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways.PLoS computational biology · 2016Article
- Systems Analyses Reveal Shared and Diverse Attributes of Oct4 Regulation in Pluripotent Cells.Cell systems · 2015Article
- From Molecules to Patients: The Clinical Applications of Translational Bioinformatics.Yearbook of medical informatics · 2015Article
- The structural basis for cancer treatment decisions.Oncotarget · 2014Review
- Big data analytics in immunology: a knowledge-based approach.BioMed research international · 2014Article
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.
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Registered trials
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.