CQS Summer Institute 2015

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2015 CQS Summer Institute

The Vanderbilt Center for Quantitative Sciences will host its second Summer Institute from August 3rd thru August 14th in the Kissam Center Classroom located near Moore and Warren College Halls.  The institute will offer four short courses focusing on biostatistics and bioinformatics.  Each course will host up to 55 participants and will provide 15 hours of lecture-based instruction and hands-on application to both Vanderbilt Medical School and nearby university faculty, staff and research fellows who are interested in learning more about applied biostatistics and bioinformatics tools.  The Summer Institute is sponsored by the Vanderbilt University School of Medicine in conjunction with the Vanderbilt Center for Quantitative Sciences. 

Dates:  Monday, August 3rd - Friday, August 14th

Times: 9:00 AM - 12:00 PM  and 1:00 PM - 4:00 PM
*Please see exact course dates and times below

Location:   Vanderbilt University (near Moore and Warren College Halls)
                   Kissam Center Classroom, C216

                   2101 West End Avenue
                  
Nashville, TN 37212

Registration Fees:    VU Student/Postdoctoral Fellow= $50.00 per course
                                    VU Faculty/Staff= $200.00 per course
                                    Non-VU Faculty/Staff/Student/Postdoctoral Fellow= $500.00 per course

Registration is now CLOSED>:  http://www.cvent.com/d/wrq1cy

CME Accredited

Course 1:  Big Data in Biomedical Research
Director: Yu Shyr, Ph.D.         
Guest Lecturers:  Lynne Berry, Ph.D. and Yan Guo, Ph.D.
Schedule: Monday, August 3rd - Friday, August 7th from 9:00 AM - 12:00 PM
Description: This course will introduce theoretical and practical challenges to be considered in designing and conducting high-dimensional experiments, including NGS, GWAS, miRNA.
Learning objectives:
After participating in this CME activity, participants should be able to:

  • Critically review and analyze Big data.
  • Identify appropriate biostatistical and bioinformatics tools for Big data study design.
  • Identify appropriate biostatistical and bioinformatics tools for Big data analysis.

Course 2:  Biostatistics I
Director: Tatsuki Koyama, Ph.D.         
Schedule: Monday, August 3rd - Friday, August 7th from 1:00 PM - 4:00 PM
Description: This course will introduce fundamental concepts and techniques for basic statistical analysis, including types of variables, data summary, hypthesis testing, simple linear regression, and power analysis.
Learning objectives: 
After participating in this CME activity, participants should be able to:

  • Critically read statistical analysis plans and analysis reports.
  • Identify study design appropriate for research question.
  • Select and perform simple statistical analysis for each study design.

Course 3:  Making Sense of High-Throughput Gene Expression Data
Co-directors: Bing Zhang, Ph.D. and Qi Liu, Ph.D.         
Schedule: Monday, August 10th - Friday, August 14th from 9:00 AM - 12:00 PM
Description: This course will introduce bioinformatics methods for the analysis and interpretation of high-throughput gene expression data and will focus on RNA-Seq data.
Learning objectives: 
After participating in this CME activity, participants should be able to:

  • Critically review and analyze high-throughput gene expression data.
  • Identify appropriate bioinformatics tools for high-throughput gene expression data analysis and interpretation.
  • Perform basic high-throughput gene expression data analysis.

Course 4:  Biostatistics II
Director:  Fei Ye, Ph.D., M.S.P.H.         
Schedule: Monday, August 10th - Friday, August 14th from 1:00 PM - 4:00 PM
Description: This course will introduce advanced statistical concepts, including logistic regression, survival analysis, repeated measures, model diagnostics, prediction models, missing data imputation, and Bayesian methods.
Learning objectives: 
After participating in this CME activity, participants should be able to:

  • Conduct appropriate statistical analysis based on type of outcome variable and data structure.
  • Build statistical models in R and perform model diagnostic analyses.
  • Interpret R output and analysis results.

Accreditation
Vanderbilt University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

Vanderbilt University School of Medicine designates this live activity for a maximum of 15 AMA PRA Category 1 Credit(s)TM. Physicians should claim only the credit commensurate with the extent of their participation in the activity.