Featured Scientist from
Essential Science IndicatorsSM
Dr. Olga Troyanskaya has been named a
in the field of Computer Science, according to an
analysis published by ScienceWatch.com in May.
Her citation record in this field in
Essential Science Indicators from
Reuters includes 31 papers cited a total of 1,533
times between January 1, 1998 and April 30, 2008. She
also has Highly Cited Papers in the field of Clinical
Medicine. Dr. Troyanskaya is an Assistant Professor in
the Department of Computer Science and the Lewis-Sigler
Institute for Integrative Genomics at Princeton
In the interview below, she
talks with us about her highly cited work.
Please tell us a little about your research and
My background is interdisciplinary—I have a Ph.D. in Biomedical
Informatics and undergraduate degrees in both Computer Science and Biology.
My research has always reflected this—I have been involved in
bioinformatics research since undergraduate days, first working with
Steven Salzberg, then at Johns Hopkins University
and The Institute for Genomic Research, and then with Gad Landau and
Alex Bolshoy at Haifa University in Israel.
The focus of my Laboratory for Bioinformatics and Computational Genomics at
Princeton University is also at the intersection of computer science and
molecular biology—in developing novel computational algorithms and
systems to address biological problems.
What do you consider the main focus of your research,
and what drew your interest to this particular area?
"One of the exciting aspects of my
work is in how dynamic the field of
computational functional genomics
My group’s research is in the area of computational functional
genomics, specifically in developing novel computational methods for the
prediction of protein function, interactions, and regulation from diverse
high-throughput biological data. We aim to develop algorithms and systems
that can make accurate predictions based on modeling and analysis of noisy
high-throughput data, as well as based on very large collections of data.
Our research is closely integrated with biology—in fact, an important
aspect of our work is the development of integrative technologies that
combine computation and experiments.
This research area is especially interesting to me because the technologies
my group develops can make a substantial impact on the fields of biology,
and in the long term, biomedicine. We develop methods that can analyze the
vast amount of available data in biological context and generate accurate,
novel predictions regarding the functions of unknown proteins or structures
of key disease pathways. Then, by using such technologies to direct
biological experiments, we hope to substantially accelerate the pace of
biological discovery, including the elucidation of functions of previously
unstudied proteins or the identification of potential disease-related
proteins and pathways.
Many of your highly cited papers deal with the analysis
of genes from microarray data. Would you talk a little about this
aspect of your research—how you got started in it, and what some
of your findings have been?
Gene expression microarrays were arguably the first technique that enabled
researchers to produce fast and relatively cheap snapshots of the
systems-level dynamics of gene regulation. I was excited about the
potential of analyzing such data to discover function for unknown proteins
and to start to examine the molecular basis of complex diseases on a
My first foray into microarray data analysis concerned a highly technical
aspect of the field: with colleagues at Stanford University, including
David Botstein and Russ Altman, we developed a method for accurately
estimating missing values in microarray datasets. Such values occur, for
example, when a specific expression level cannot be reliably determined in
the microarray experiment. The missing value estimation method we
developed, KNNimpute, is still widely used in the research community. Since
then, our work has included the analysis of multiple clinical datasets,
focusing on identifying clinically relevant biomarkers and finding
chromosomal amplification and deletions (both from array CGH and gene
expression microarray studies), pathway modeling, and most recently,
analysis of very large microarray compendia.
I am excited about the potential of performing sophisticated computational
analyses of the large existing collections of microarray and other
functional genomics data to answer questions that are often very hard to
address by a single study. This is one of the directions of our recent
work, including developing a "Google"-type search engine for microarrays
(SPELL) and probabilistic Bayesian systems for the
analysis of diverse functional genomics data
Your most-cited paper in our database in the field
of Computer Science is the 2003 PNAS paper, "A Bayesian
framework for combining heterogeneous data sources for gene function
prediction (in Saccharomyces cerevisiae)." Would you give our
readers some background on this paper—its goals and
findings—and why it is so popular?
"...an important aspect of our work
is the development of integrative
technologies that combine computation and
This manuscript describes a probabilistic Bayesian method for the
integration of diverse genome-scale data into confidence-weighted
functional relationship networks among proteins, which can then be used to
predict protein function. We demonstrated the principle of probabilistic
data integration for functional genomics data, and this is now a very large
and active research area, in which my group is still very involved.
Since then, we have developed a full-scale learning-based system for
integration of heterogeneous biological data and prediction of protein
function and functional relationship networks (bioPIXIE); this system is
widely used by the yeast community. We have also introduced the idea of
exploring such questions in the context of specific pathways or tissues, as
proteins can have multiple functions in different biological contexts.
How has this field changed since you first started
working in it?
One of the exciting aspects of my work is in how dynamic the field of
computational functional genomics is. New experimental techniques,
biological questions, and computational approaches are constantly coming
out, and a diverse group of highly interdisciplinary researchers aims to
address these challenges through a variety of approaches. Compared with a
decade ago, two key differences are perhaps in the increasing
sophistication of the computational methods and in the much closer tie-in
of most studies with the experimental biology.
Where do you see this work going in five to ten
We are still far from understanding the full complexity of gene function
and regulation on a systems level, and my aim is to continue developing
integrated computational and experimental technologies for addressing this
problem. Our goal is to map cellular regulatory structures and develop
predictive models for effects of genetic and environmental perturbations.
Looking at the long term, my hope is that computational methods will guide
genome-scale explorations of complex molecular, cellular, and organismic
systems at complementary levels of resolution, some day leading us to
integrate our understanding of microscopic biology with macroscopic
physiology and medicine.
Olga Troyanskaya, Ph.D.
Department of Computer Science
Lewis-Sigler Institute for Integrative Genomics
Princeton, NJ, USA
Troyanskaya's most-cited paper
with 390 cites to date:
Garber ME, et al., "Diversity of gene expression
in adenocarcinoma of the lung," Proc. Nat. Acad. Sci.
USA 98(24): 13784-9, 20 November 2001. Source:
Essential Science Indicators from
Keywords: bioinformatics, interdisciplinary research,
computer science, molecular biology, computational algorithms,
computational functional genomics, protein function, gene expression