Creative Proteomics services

Proteins are vital macromolecules, the workhorses that facilitate most biological processes at both cellular and systemic levels, but they rarely act alone. Lots of essential molecular processes are carried out by molecular machines that are based on a large number of protein components organized by their Protein-Protein Interactions(PPIs), which refer to intentional physical contacts established between two or more proteins as a result of biochemical events and/or electrostatic forces. Since the interactions are at the core of the entire interactomics system of any living cell, unsurprisingly, specific PPIs are on the basis of multiple diseases.

KEGG, abbreviation of Kyoto Encyclopedia of Genes and Genomes, is a collection of databases, which is used for bioinformatics research, including data mining in genomics, proteomics, metabolomics and other omics studies, modeling and simulation in systems biology, and translational research in drug R & D.

Gene ontology, GO for short, is a quite powerful bioinformatics initiative to unify the representation of gene and gene product attributes across all species. Gene Ontology is established by Gene Ontology Consortium in 2008 in order to annotate and classify genes and their corresponding products, mainly under 3 terms: molecular functions, cellular components and biological processes. More specifically, the project aims to: Maintain and develop its controlled vocabulary of gene and gene product attributes. Annotate genes and gene products, and assimilate and disseminate annotation data. Provide tools for easy access to all aspects of the data, and enable functional interpretation of experimental data.

In data mining, cluster analysis is used to classify a set of observations into two or more mutually exclusive unknown groups, based on combinations of the interval variables. The purpose, is to discover a system of organizing observations, usually genes, and proteins into groups, where members of the groups share properties in common. In Creative Proteomics, we can interpret the data you collected with a set of typical clustering methodologies, algorithms, and applications, which include partitioning methods such as k-means, hierarchical methods and density-based methods. Your data can be interpreted and visualized with our assistance.