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

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bhattacharya FrontInOnc2019 9.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Anx1 WHO 2020 2020.5.png|240px]]</div>
'''"[[Journal:AI meets exascale computing: Advancing cancer research with large-scale high-performance computing|AI meets exascale computing: Advancing cancer research with large-scale high-performance computing]]"'''
'''"[[Journal:Laboratory testing for coronavirus disease (COVID-19) in suspected human cases|Laboratory testing for coronavirus disease (COVID-19) in suspected human cases]]"'''


The application of data science in [[cancer]] research has been boosted by major advances in three primary areas: (1) data: diversity, amount, and availability of biomedical data; (2) advances in [[artificial intelligence]] (AI) and machine learning (ML) algorithms that enable learning from complex, large-scale data; and (3) advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build ''in silico'' ML models that can provide transformative insights from data, including molecular dynamics simulations, [[Sequencing|next-generation sequencing]], omics, [[Molecular imaging|imaging]], and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next-generation high-performance computers; and (3) assessing robustness and reliability in the AI models. ('''[[Journal:AI meets exascale computing: Advancing cancer research with large-scale high-performance computing|Full article...]]''')<br />
This document provides interim guidance to [[Laboratory|laboratories]] and stakeholders involved in [[COVID-19]] virus laboratory testing of patients. It is based in part on the interim guidance on laboratory testing for [[Middle East respiratory syndrome]] (MERS) coronavirus. [[Information]] on human [[infection]] with the COVID-19 virus is evolving and the [[World Health Organization]] (WHO) continues to monitor developments and revise recommendations as necessary. This document will be revised as new information becomes available. Feedback is welcome and can be sent to WHElab@who.int. The virus has now been named SARS-CoV-2 by the International Committee of Taxonomy of Viruses (ICTV)(2). This virus can cause the disease named coronavirus disease 2019 (COVID-19). WHO refers to the virus as COVID-19 virus in its current documentation. ('''[[Journal:A security review of local government using NIST CSF: A case study|Full article...]]''')<br />
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Revision as of 15:46, 23 March 2020

Anx1 WHO 2020 2020.5.png

"Laboratory testing for coronavirus disease (COVID-19) in suspected human cases"

This document provides interim guidance to laboratories and stakeholders involved in COVID-19 virus laboratory testing of patients. It is based in part on the interim guidance on laboratory testing for Middle East respiratory syndrome (MERS) coronavirus. Information on human infection with the COVID-19 virus is evolving and the World Health Organization (WHO) continues to monitor developments and revise recommendations as necessary. This document will be revised as new information becomes available. Feedback is welcome and can be sent to WHElab@who.int. The virus has now been named SARS-CoV-2 by the International Committee of Taxonomy of Viruses (ICTV)(2). This virus can cause the disease named coronavirus disease 2019 (COVID-19). WHO refers to the virus as COVID-19 virus in its current documentation. (Full article...)

Recently featured:

One tool to find them all: A case of data integration and querying in a distributed LIMS platform
What is the "source" of open-source hardware?
From command-line bioinformatics to bioGUI