Difference between revisions of "Template:Article of the week"

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(Updated article of the week text.)
(Updated article of the week text.)
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Malykh JofHealthEng2018 2018.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Mulder PLOSCompBio2018 14-2.png|240px]]</div>
'''"[[Journal:Approaches to medical decision-making based on big clinical data|Approaches to medical decision-making based on big clinical data]]"'''
'''"[[Journal:The development and application of bioinformatics core competencies to improve bioinformatics training and education|The development and application of bioinformatics core competencies to improve bioinformatics training and education]]"'''


The paper discusses different approaches to building a [[clinical decision support system]] based on big data. The authors sought to abstain from any data reduction and apply universal teaching and big data processing methods independent of disease classification standards. The paper assesses and compares the accuracy of recommendations among three options: case-based reasoning, simple single-layer neural network, and probabilistic neural network. Further, the paper substantiates the assumption regarding the most efficient approach to solving the specified problem. ('''[[Journal:Approaches to medical decision-making based on big clinical data|Full article...]]''')<br />
[[Bioinformatics]] is recognized as part of the essential knowledge base of numerous career paths in biomedical research and healthcare. However, there is little agreement in the field over what that knowledge entails or how best to provide it. These disagreements are compounded by the wide range of populations in need of bioinformatics training, with divergent prior backgrounds and intended application areas. The Curriculum Task Force of the International Society of Computational Biology (ISCB) Education Committee has sought to provide a framework for training needs and curricula in terms of a set of bioinformatics core competencies that cut across many user personas and training programs. The initial competencies developed based on surveys of employers and training programs have since been refined through a multiyear process of community engagement. This report describes the current status of the competencies and presents a series of use cases illustrating how they are being applied in diverse training contexts. ('''[[Journal:The development and application of bioinformatics core competencies to improve bioinformatics training and education|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'':
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Revision as of 18:25, 2 January 2019

Tab1 Mulder PLOSCompBio2018 14-2.png

"The development and application of bioinformatics core competencies to improve bioinformatics training and education"

Bioinformatics is recognized as part of the essential knowledge base of numerous career paths in biomedical research and healthcare. However, there is little agreement in the field over what that knowledge entails or how best to provide it. These disagreements are compounded by the wide range of populations in need of bioinformatics training, with divergent prior backgrounds and intended application areas. The Curriculum Task Force of the International Society of Computational Biology (ISCB) Education Committee has sought to provide a framework for training needs and curricula in terms of a set of bioinformatics core competencies that cut across many user personas and training programs. The initial competencies developed based on surveys of employers and training programs have since been refined through a multiyear process of community engagement. This report describes the current status of the competencies and presents a series of use cases illustrating how they are being applied in diverse training contexts. (Full article...)

Recently featured:

Approaches to medical decision-making based on big clinical data
A new numerical method for processing longitudinal data: Clinical applications
Big data management for healthcare systems: Architecture, requirements, and implementation