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KISTI is a government-funded research institute designed to maximize the efficiency of science and technology R&D and support high-tech R&D for researchers. KISTI's Supercomputing Center provides acess to its supercomputing services,  which operate on the highly advanced infrastructure of KREONET, to Korea's university researchers, institutes, industries, and government organizations.  In addition to all these services, the center also provides practical applied technology so that we may compete in the field of government science and promote national welfare.


The Bioinformatics team in KISTI have been constructing the cyber research environments including insilicoCell, Bioworks, etc. To promote bioinformatics studies as well as biological researches, we will consistently develop bioinformatics tools and reliable services using our supercomputer resources.

 

Bioworks

Bioworks is a system to effectively model and automate bioinformation research process that is performed in the form of workflow.  User can integratively perform the bioinformation analysis process ...

 

insilicoCell

insilicoCell project is aimed at developing a integrated system for generating biological interaction and simulation tools. The most biologists want to get a novel biological pathway to make a new hypothesis and experiment it.

 

 

Parameter Space Explorer

PSExplorer is an efficient computational tool to explore high dimensional parameter space of computational models for identifying qualitative behaviors and key parameters.

MOdularized NETwork Learning(MONET)

MONET is a Cytoscape plugin to infer genome-scale networks from gene expression profiles. It alleviates the shortage of information by incorporating pre-existing annotations.

RBSDesigner

We developed RBSDesigner based on a mathematical model on translation initiation to design synthetic ribosome binding sites that yield a desired level of expression of user-specified coding sequences.

SBN(Switching Boolean Networks) simulator

Switching Boolean Networks Simulator(SBNsimulator) was developed to simulate large-scale signaling network.

Multi-level Knowledge Emergence Model

We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap.

 
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