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 Swaminathan FrontInGenetics2018 9.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Talia JOfCloudComp2019 8.png|240px]]</div>
'''"[[Journal:Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital|Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital]]"'''
'''"[[Journal:A view of programming scalable data analysis: From clouds to exascale|A view of programming scalable data analysis: From clouds to exascale]]"'''


The adoption rate of [[Genomics|genome sequencing]] for clinical diagnostics has been steadily increasing, leading to the possibility of improvement in diagnostic yields. Although [[Laboratory|laboratories]] generate a summary clinical report, sharing raw genomic data with healthcare providers is equally important, both for secondary research studies as well as for a deeper analysis of the data itself, as seen by the efforts from organizations such as American College of Medical Genetics and Genomics, as well as Global Alliance for Genomics and Health. Here, we aim to describe the existing protocol of genomic data sharing between a certified [[clinical laboratory]] and a healthcare provider and highlight some of the lessons learned. This study tracked and subsequently evaluated the data transfer workflow for 19 patients, all of whom consented to be part of this research study and visited the genetics clinic at a tertiary pediatric hospital between April 2016 and December 2016. ('''[[Journal:Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital|Full article...]]''')<br />
Scalability is a key feature for big data analysis and machine learning frameworks and for applications that need to analyze very large and real-time data available from data repositories, social media, sensor networks, smartphones, and the internet. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of high-performance computing (HPC) systems and [[cloud computing]] systems, whereas in the near future exascale systems will be used to implement extreme-scale [[data analysis]]. Here is discussed how cloud computing currently supports the development of scalable data mining solutions and what the main challenges to be addressed and solved for implementing innovative data analysis applications on exascale systems currently are. ('''[[Journal:A view of programming scalable data analysis: From clouds to exascale|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'':
: ▪ [[Journal:Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital|Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital]]
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: ▪ [[Journal:Research on information retrieval model based on ontology|Research on information retrieval model based on ontology]]
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Revision as of 15:13, 8 April 2019

Fig1 Talia JOfCloudComp2019 8.png

"A view of programming scalable data analysis: From clouds to exascale"

Scalability is a key feature for big data analysis and machine learning frameworks and for applications that need to analyze very large and real-time data available from data repositories, social media, sensor networks, smartphones, and the internet. Scalable big data analysis today can be achieved by parallel implementations that are able to exploit the computing and storage facilities of high-performance computing (HPC) systems and cloud computing systems, whereas in the near future exascale systems will be used to implement extreme-scale data analysis. Here is discussed how cloud computing currently supports the development of scalable data mining solutions and what the main challenges to be addressed and solved for implementing innovative data analysis applications on exascale systems currently are. (Full article...)

Recently featured:

▪ Transferring exome sequencing data from clinical laboratories to healthcare providers: Lessons learned at a pediatric hospital
▪ Research on information retrieval model based on ontology
▪ Data to diagnosis in global health: A 3P approach
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