Cancer Informatics
Synopsis: An open access, peer reviewed electronic journal that covers the role of computational biology and bioinformatics in cancer treatment.
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About this journal
Aims and scope:
We live in a time when it is widely recognized that scientific collaborations across traditional disciplines can yield exponential gains through synergy. Cancer research is currently benefiting from advances in many fields, including biology (genomics and proteomics), physical chemistry (mass spectrometry and radio imaging), computer science and biostatistics (machine learning, artificial intelligence), and many others.
There exists a bewildering diversity of scientific journals in which new applications of these advances toward discovery in cancer research is reported. Leveraging these advances into new medicines and medical practices for the early detection, prevention and treatment of cancer is made difficult by the broad diffusion this literature. Bioinformatics and computational biology appear to play a central role at each nexus, in part because novel technologies lead to immense leaps in the amount and granularity of data from patients and patient samples.
A number of important journals exist that focus on a wide breadth of foci within bioinformatics. Medical informatics is a field nearly as wide that includes patient information systems and related critical components of information management in the information age.
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Submissions, excluding editorials, letters to the editor and dedications, will be peer reviewed by two reviewers. Reviewers are required to provide fair, balanced and constructive reports.
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Journal newsletter sent on week 3, 2009
Uncovering Biological Network Function via Graphlet Degree Signatures
Tijana Milenković and Nataša Pržulj
Department of Computer Science, University of California, Irvine, CA 92697-3435, USA
Abstract
Motivation: Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker’s yeast. Methods for determining protein function have shifted their focus from targeting specific proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI) networks. Since proteins interact to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines.
Results: We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method summarizes a protein’s local topology in a PPI network into the vector of graphlet degrees called the signature of the protein and computes the signature similarities between all protein pairs. We group topologically similar proteins under this measure in a PPI network and show that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassified proteins demonstrating that our method can provide valuable guidelines for future experimental research such as disease protein prediction.
Availability: Data is available upon request.
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