Tag Archives: SLaM

Big Data in Mental Health Symposium @ KCL/SLaM – Live Stream

http://maudsley.mediasite.ulcc.ac.uk/Mediasite/Play/b6a747dab20e47c2af28f55c70c252851d

Symposium on Big Data in Mental Health

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For anyone interested in data analytics, the BRC-MH at KCL and the Maudsley are organising a symposium on Big Data in Mental Health on July 23rd at ORTUS, Denmark Hill.

Talks will include Maneesh Juneja, Chris Hollis from MindTech, Will Spooner from Eagle Genomics and speakers from the University of Oxford, KCL and the SLaM.

More details available on the website: http://core.brc.iop.kcl.ac.uk/events/big-data-in-mental-health/

Registration on Eventbrite: https://www.eventbrite.co.uk/e/big-data-in-mental-health-biomedical-research-tickets-11667875931

June Tech Meet

KCL Logo

SLaM Logo
 
 
 

This month’s tech meet, organised in association with the KCL Bioinformatics Interest Group (BIG) and the Biomedical Research Centre for Mental Health (BRC-MH), will be at King’s College London, Guy’s Campus (London Bridge) in the Large Committee Room of the Hodgkin Building from 6pm on Thursday 23rd June.

We’ll have some tables booked at The Miller afterwards for more biogeeky chat and beers.

Important!

We only have space for 35 people to participate in the WGCNA talk / workshop, and we expect it to be a popular event, so please email london.biogeeks@gmail.com if you want to be sure of a space.

If you can’t make it to the workshop, feel free to come along to the pub afterwards. We’ll be there from around 7.30.


Steve Horvath

 

Speaker

We’re excited to announce that this month’s meet up will feature Steve Horvath from the Biostatistics Department at UCLA.

Steve is a Professor in Human Genetics and Biostatistics and his group develops biostatistical, computational, and systems biologic methods for studying complex phenotypes. Recent research has focused on the development and application of systems biologic and systems genetic methods for addressing biological, genetic and clinical questions. He’ll be giving us an overview of Weighted Gene Co-Expression Network Analysis (WGCNA).

Bring along a laptop with R and WGCNA installed so you can play along!

 
 


WGCNA

WGCNA

Weighted gene co-expression network analysis (WGCNA) facilitates a systems biologic view of gene expression data. The network framework makes it straightforward to integrate gene expression data with other types of data, e.g. clinical traits and genetic marker data. This talk covers several theoretical topics including network construction, module definition, network based gene screening, and differential network analysis. The methods are illustrated using several applications including i) screening for cancer genes, ii) comparing human and chimp brains, and iii) complex disease gene mapping. Related articles and material can be found at the following webpage http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/