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Hiroaki Kitano

Researcher at Okinawa Institute of Science and Technology

Publications -  335
Citations -  35176

Hiroaki Kitano is an academic researcher from Okinawa Institute of Science and Technology. The author has contributed to research in topics: Systems biology & Humanoid robot. The author has an hindex of 67, co-authored 323 publications receiving 32886 citations. Previous affiliations of Hiroaki Kitano include University of Manchester & Keio University.

Papers
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Journal ArticleDOI

Systems biology: a brief overview.

Hiroaki Kitano
- 01 Mar 2002 - 
TL;DR: To understand biology at the system level, the authors must examine the structure and dynamics of cellular and organismal function, rather than the characteristics of isolated parts of a cell or organism.
Journal ArticleDOI

The Transcriptional Landscape of the Mammalian Genome

Piero Carninci, +197 more
- 02 Sep 2005 - 
TL;DR: Detailed polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.
Journal ArticleDOI

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models.

TL;DR: This work summarizes the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks, a software-independent language for describing models common to research in many areas of computational biology.
Journal ArticleDOI

Computational systems biology

Hiroaki Kitano
- 14 Nov 2002 - 
TL;DR: The reviews in this Insight cover many different aspects of this energetic field, although all, in one way or another, illuminate the functioning of modular circuits, including their robustness, design and manipulation.
Proceedings Article

Biological robustness

Hiroaki Kitano, +1 more
TL;DR: Insights into inherent properties of robust systems will provide a better understanding of complex diseases and a guiding principle for therapy design.