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“My paper presents a systematic comparison of some of the most popular data analysis methods, and presents the reader with the relative advantages and disadvantages of various methods.”
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I believe my paper is highly cited because it deals with data
analysis in a very popular area of molecular biology research:
gene expression profiling. This technique enables the
simultaneous measurement of expression levels of thousands of
genes. Such data can be used in the study of disease processes,
drug mechanism of action, etc., at a biomolecular level. The
number of papers published in this area is growing at an
exponential rate.
Does
it describe a new discovery or a new methodology that's useful to
others?
My paper does not describe a new discovery or methodology;
rather it presents a comparison of data analysis methods for
gene-expression data measured using a popular technology known
as AffymetrixTM oligonucleotide arrays.
Could
you summarize the significance of your paper in layman's terms?
Gene expression profiling experiments present a considerable
challenge in data analysis. Many statistical methods have been
developed to address this challenge. It is not clear which of
these methods gives the best results. My paper presents a
systematic comparison of some of the most popular data-analysis
methods, and presents the reader with the relative advantages
and disadvantages of various methods.
How
did you become involved in this research?
I work in the Bioinformatics department at GlaxoSmithKline,
the second largest pharmaceutical company in the world. Gene
expression profiling using microarrays is an important
technology we utilize in our drug discovery research. It is very
important that our scientists use the best possible techniques
for data analysis. This research helps my colleagues determine
the best possible method to use for analysis of their
experimental data.
Dilip Rajagopalan
GlaxoSmithKline R&D/Bioinformatics
King of Prussia, PA, USA
