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Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9029)

Abstract

Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or proteins.

Keywords

Support Vector Machine Biological Network Complex Biological System Protein Function Prediction Random Walk With Restart 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Cao, M., et al.: New directions for diffusion-based network prediction of protein function: incorporating pathways with confidence. Bioinformatics 30(12), i219–i227 (2014)CrossRefGoogle Scholar
  2. 2.
    Mostafavi, S., et al.: GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol 9, S1–S4 (2008)CrossRefGoogle Scholar
  3. 3.
    Milenkoviæ, T., Pržulj, N.: Uncovering biological network function via graphlet degree signatures. Cancer Informatics 6, 257 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Computer Science and Artificial Intelligence LaboratoryMITCambridgeUSA
  2. 2.Department of MathematicsMITCambridgeUSA
  3. 3.Department of Computer ScienceUniversity of Illinois at Urbana-ChampaignChampaignUSA

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