Event Title

Internship Experience in Computational Biology at Van Andel Institute

Location

Exhibition Hall, DeVos Center

Start Date

10-4-2012 3:00 PM

Description

PURPOSE: While satisfying the internship requirement for the GVSU Professional Science Masters in Biostatistics, this internship experience involved statistical support for biostatistics and bioinformatics projects for the Computational Biology Lab and collaborators of Dr. Kyle Furge at the Van Andel Institute. CHALLENGE: In a laboratory setting, the work involved learning new software and techniques in bioinformatics and gene expression analysis, while also learning and understanding unfamiliar concepts in biology. EXPERIENCE and OUTCOMES: Statistical genetics and associated statistical computing methodology are rapidly advancing areas in applied statistics. Extremely valuable experience in the analysis of gene expression micro-array data was obtained. Utilizing R software and specialized R packages, techniques such as survival analysis, pathway analysis and classification/clustering were applied. IMPACT: While completing the internship requirement of the Masters degree, obtaining valuable experience in an industry/research setting was a great opportunity. Methodology involving micro-array gene expression analysis was learned and applied to real-life examples.

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Apr 10th, 3:00 PM

Internship Experience in Computational Biology at Van Andel Institute

Exhibition Hall, DeVos Center

PURPOSE: While satisfying the internship requirement for the GVSU Professional Science Masters in Biostatistics, this internship experience involved statistical support for biostatistics and bioinformatics projects for the Computational Biology Lab and collaborators of Dr. Kyle Furge at the Van Andel Institute. CHALLENGE: In a laboratory setting, the work involved learning new software and techniques in bioinformatics and gene expression analysis, while also learning and understanding unfamiliar concepts in biology. EXPERIENCE and OUTCOMES: Statistical genetics and associated statistical computing methodology are rapidly advancing areas in applied statistics. Extremely valuable experience in the analysis of gene expression micro-array data was obtained. Utilizing R software and specialized R packages, techniques such as survival analysis, pathway analysis and classification/clustering were applied. IMPACT: While completing the internship requirement of the Masters degree, obtaining valuable experience in an industry/research setting was a great opportunity. Methodology involving micro-array gene expression analysis was learned and applied to real-life examples.