Rigorous quantitative sciences integration--the foundation of high-dimensional genomic research.
With the recent rapid increases in the high-dimensionality of genomic data generation comes an increased burden on the biostatisticians and bioinformaticians who process and analyze this data. Study designs must be adapted to the volume of data now available, eliminating designs that rely on fishing and taking advantage of the massive amounts of publically available genomic data through data-mining. Most importantly, it is no longer sufficient to have a single person handling the data analysis. To get the breadth of expertise needed to analyze high-dimensional data and to have the appropriate checks to eliminate the costly mistakes that are so easy to make when handling this volume of data, specialists from many different areas of quantitative sciences must be brought together to approach high-dimensional data analysis as an integrated team.
Vanderbilt University Medical Center, Nashville, TN, USA. yu.shyr@vanderbilt.edu
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D000818:Animals; D002460:Cell Line; D019295:Computational Biology; D057225:Data Mining; D023281:Genomics; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D012106:Research; D013223:Statistics as Topic