若手研究者インターナショナル・トレーニング・プログラム(ITP)
バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム 報告書

Name：Sayaka Mizutani
Title：Analysis of the correlation between drug side-effects and drug-target interactions.
Institute: Bioinformatics Center, Institute for Chemical Research, Kyoto University
Partner institute: Centre for Computational Biology, Mines ParisTech
Duration: September 30, 2010 – December 24, 2010
Report:1.

General report
Supported by the ITP program, I stayed for three month at Mines ParisTech, Centre for
Computational Biology, directed by Dr. Jean-Philippe Vert. During the stay I was involved in a
research project under the supervision of Dr. Yoshihiro Yamanishi, a researcher at the laboratory.
The Center for Computational Biology has a joint research program with Institut Curie. InstitutCurie is one of the leading medical and biological research institutes in Paris and a clinical institutespecialized in the treatment of cancer. Faculty members and many students in the Centre for
Computational Biology run collaborative research projects with experimental biologists in theinstitute. In the projects they develop theoretical and computational framework for the analyses oflarge-scale data. Staying in Institut Curie would be a good choice for me to experience theatmosphere of collaborative research between computational biology and experimental biology.
Therefore, I chose to stay at Institut Curie in Paris.
During the stay I participated in weekly lab
meetings, where students and faculty members are
responsible for a brief progress report. Every meeting
was also devoted for a longer presentation time, inwhich one of the members is expected to give a detailed
talk on their research projects. From the meetings I wasexposed to many new concepts and ideas in theMachine Learning field. Also the meetings were perfectopportunities for me to exchange opinions with the lab
members over each other’s research topics. I also participated in a reading group on Statistics andMachine Learning topics.
In Institut Curie, there were open access seminars, which are organized by several different
laboratories. These seminars cover many research fields including Molecular, DevelopmentalBiologies, Oncology and other medical studies. Some of such seminars, that were impressive tome, were given by Dr. Edith Heard from Institut Curie, on the inactivation of X chromosome, andby Dr. Reinhard Laubenbacher from Virginia Bioinformatics Institute, on algebraic modeling ofiron metabolism. There was also a talk given by Dr. Elizabeth Blackburn from University ofCalifornia, San Francisco, the 2009 Nobel Prize laureate for the discovery of telomerase. Theseseminars were valuable opportunities to learn about interests and ideas from experimental

若手研究者インターナショナル・トレーニング・プログラム(ITP)
バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム 報告書

biologists, from which I could search for possibilities for computational biology methods to apply
to problems that may occur in the analyses of large-scale data.
Furthermore, I had a few chances to listen to Statistical Machine Learning lectures at l'EcoleNormale Supérieure located a few minutes from Institut Curie. Especially it was a very valuable
experience to listen to a lecture from Dr. Trevor Hastie, who is one of the most famous researchers
in the Statistical Machine Learning field.
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2. Research projectIn the first two weeks of stay I worked on abreast cancer data published by Chin et al. in2006 [1]. The dataset consists of three types ofdata; microarray expression, array CGH, anddiagnostic data. Our goal was to extract genes
whose expressions are correlated with the copy
numbers of the corresponding chromosomal
areas. In order to predict cancer progressive
stages based on the extracted genes weperformed a canonical correlation analysis(CCA) and a support vector machine. However,
we found that the calculation would take Figure 1. Histograms of the frequency of genes whose
are correlated with copy numbers. Red
several months to finishi, due to the large size expressions
stars indicate chromosomes for which mostly trueof the data. Therefore, I had to change my positives were gained.
research topic.
Next, I became involved in a project organized by Dr. Yoshihiro Yamanishi and his colleagues,
Dr. Véronique Stoven and Edouard Pauwels from Mines ParisTech. Their interest is to develop
statistical models that can be applied to a system-wide analysis of multiple drug-related data ofdifferent structures. Drug-related database such as DrugBank [2] accumulates different types ofdrug-attributed data such as chemical structures, target proteins, and drug indications. Linking suchdifferent data may provide molecular and physiological explanations how drugs act in the humanbody. From such a point of view, Yamanishi and colleagues have been worked on the developmentof statistical machine learning tools to investigate relationships between different types ofdrug-attributed data. In previous work, they proposed a method to predict pharmacological effectinformation of drugs using their chemical structures [3]. However, this approach does not shedlight on the mechanism in which drug-target protein interactions cause side-effects. Thus, we weremotivated to investigate correlations between drug target proteins and their side-effects. Extractionof correlated sets of target proteins and side-effects is made possible by considering a CCA. Given
two sets of variables from the same set of samples, CCA solves for the variables in which thecorrelation between the two sets of variables is maximized. Moreover, more recent version of themethod has been proposed, in which only a few variables are extracted [4]. We applied these05101520

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若手研究者インターナショナル・トレーニング・プログラム(ITP)
バイオインフォマティクスとシステムズバイオロジーの国際連携教育研究プログラム 報告書

methods to our datasets. Cross validation indicated that our methods have high prediction
accuracies. We also validated the contents of extracted sets of target proteins and side-effects froma biological point of view. As a result, we came to a conclusion that our method can be applied tothe prediction of potential side-effects for drugs based on their target protein profiles. Then, usingtarget protein profiles of drugs whose side-effects are unknown, we predicted potential side-effects
for these drugs. The result has been submitted to ISMB2011.
References:[1] Chin, K. et al., (2006) Genomic and transcriptional aberrations linked to breast cancer
pathophysiologies, Cancer Cell, 10:529–541.
[2] Wishart, D., Knox, C., Guo, A., Shrivastava, S., Hassanali, M., Stothard, P., Chang, Z. &Woolsey, J. (2006) DrugBank: a comprehensive resource for in silico drug discovery andexploration. Nucleic Acids Research, 34, D668–D672.
[3] Yamanishi, Y., Kotera, M., Kanehisa, M. & Goto, S. (2010) Drug-target interaction
prediction from chemical, genomic and pharmacological data in an integrated framework.
Bioinformatics, 26, i246–i254.
[4] Witten, D., Tibshirani, R. & Hastie, T. (2009) A penalized matrix decomposition, withapplications to sparse principal components and canonical correlation analysis. Biostatistics,
10, 515-534.
Acknowledgement:
I would like to express my deepest gratitude to Professor Minoru Kanehisa and Professor Hiroshi
Mamitsuka, and Associate Professor Susumu Goto for providing me a valuable learning experiencethrough the ITP program. I would also thank Dr. Jean-Philippe Vert, Dr. Yoshihiro Yamanishi andtheir colleagues for kindest supports for my stay in Institut Curie.

From the left; Dr. Jean-Philippe Vert, Sayaka Mizutani,

A snap shot from a weekly lab meeting at The Centre forDr. Véronique Stoven and Dr. Yoshihiro Yamanishi.

Computational Biology.