Colloquium on Genome Data Analysis Methods

UCLA is hosting a free 1-day colloquium on "Systems Biology Analysis Methods for Genomic Data"
on Tuesday, October 7th, 2014. Click here for schedule details and information on registration.

Genome Data Analysis Colloquium

UCLA is hosting a 1-day colloquium on Tuesday, October 7th 2014, on Systems Biology Analysis Methods for Genomic Data at the Center for the Health Sciences (13-105 CHS), University of California Los Angeles. 

This full day event is open to the public, but space is limited. To register for this event send an email with subject line "Network Analysis" to

Speakers will discuss network analysis methods widely used in systems biologic and systems genetic applications. The goal is to familiarize researchers with network methods and software for integrating genomic data sets with complex phenotype data. Participants will learn how to integrate disparate data sets (genetic variation, gene expression, complex phenotypes, gene ontology information) and use networks for identifying disease genes, pathways and key regulators. We also describe applications to DNA methylation data and the epigenetic clock software.

This full day event is open to the public, but space is limited. To register for this event send an email with subject line "Network Analysis" to


SHorvathSteve Horvath, PhD

Professor of Human Genetics & Biostatistics
PLangfelderPeter Langfelder, PhD
Department of Human Genetics

9:30am - 10:30am: Hierarchical clustering and the dynamic tree cut algorithm. Peter Langfelder

10:45am - noon: Weighted correlation network analysis. Steve Horvath

Noon - 1:30pm Break for lunch

1:30pm - 2:15pm: Consensus network analysis. Peter Langfelder

2:20pm - 3:15pm: R software tutorial for WGCNA. Steve Horvath

3:30pm - 4:15pm: Systems genetic analysis with structural equation models.Steve Horvath

4:30pm - 5:15pm: The epigenetic clock and user-friendly software. Steve Horvath

Advance notice: A week-long Network workshop will be held on the UCLA campus in the summer
of 2015. If you wish to be on the mailing list for this and future events, please email

Summer 2014 in Chang Liu's Lab

See the youtube video below about how one talented Orange County high school student spent his summer working with Dr. Chang Liu (Department of Biomedical Engineering/CCBS).

Broad Institute Computational Genomics Workshop

Broad CCCbanner-2014The Center for Cell Circuits is hosting an outreach computational genomics workshop at the Broad Institute, Cambridge, MA, for 2 days on September 22nd and 23rd. The workshop is aimed at reaching out to the external community and educating attendees on computational tools developed to analyze genome-scale data sets at multiple levels and to reconstruct cell circuit models from these large scale data sets. Day 1 is a lecture series followed by a limited capacity hands-on training the second day. In order to attend Day 2, attendees must go to Day 1 lecture series.

This workshop is open to everyone in the academic community (faculty, staff, post-doc fellows, PhD candidates) and has no registration costs. Travel grants are available for attendees to help cover costs.

Registration through website link below:



High-school student Kevin Lee wins 2nd Place at the 2014 Intel STS Finalist

Kevin LeeCongratulations to University High School student Kevin Lee from Ivine on winning 2nd Place at the national 2014 Intel Science Talent Search (STS) competition for his project entitled "Electromechanical modeling of the heart in moving domains using the phase-field method." Kevin was mentored by UCI professor John Lowengrub (CCBS/Math/BME/ChEMS). He will receive a $75,000 scholarship.

Kevin's project focuses on the development of a new theory of the heartbeat through a system of partial differential equations. Cardiac arrhythmias are the leading cause of death in the industrialized world but are not well-understood due to difficulties in linking the physical beating motion of the heart with the propagation of electric signals, and vice versa.

This work successfully couples the mechanical and electrical dynamics and develops an algorithm that enables much more efficient simulations of the heartbeat than those in use today. The added insights from the model promise to improve our understanding of fatal heart conditions and ultimately aid in their treatment and prevention.

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