Name: Training Course on Analysis of “big data” in biomedical sciences: possibilities and challenges
Start: 2015-06-25T10:00:00+01:00
End: 2015-06-26T15:00:00+01:00
Location: Universidad de la Laguna

Acerca del evento

Del 25-06-2015 al 26-06-2015

Aula A1. Facultad de Ciencia de la Salud. Sección Medicina, universidad de la laguna, tenerife

Categorías:: Calidad, I+D e innovación

Palabras clave:: big data; Biomedical; Statistics

<img alt="" src="/_files/_event/_2490/_editorFiles/file/Logo cibican color.jpg" style="width: 150px; height: 83px;" />         <img alt="" src="/_files/_event/_2490/_editorFiles/file/Logo Imbrain color.jpg" style="width: 200px; height: 86px;" />           <img alt="" src="/_files/_event/_2490/_editorFiles/file/Logo_Europa.png" style="width: 100px; height: 67px;" />

             

 This activity is promoted by CIBICAN, funded by the IMBRAIN project 

 The recent irruption of genomics, proteomics and metabolomics in biomedical research allows us to collect thousands of variables for each patient where not long ago we would collect only dozens. At the same time, more and more biomedical databases are been made available for anyone to use freely, ranging from databases of affinity of between biochemical molecules and proteins to actual human data from randomised clinical trials.  The combination of all these data can produce valuable information to inform the design of future studies.

 The challenges lie in how to exploit and integrate so many and so large data sets. How do we determine what variables are important to answer a certain question when there are so many available? How do we make the connections from one kind of data to another? Efficient management and analysis requires the application and adaptation of techniques that are not commonly used in biomedical research, such as, machine learning methods, or database connectivity and integration.  This will require the combination of different skills such as statistics, machine learning algorithms and bioinformatics. There is a need for a new kind of experts that can work along with biologists and clinicians to unlock the information contained in the new “big data” world of the biomedical sciences.

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