Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at http://www.ucl.ac.uk/finance/research/post_award/post_award_contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
Gene Function Prediction from Functional Association Networks Using Kernel Partial Least Squares Regression.
  • Publication Type:
    Journal article
  • Publication Sub Type:
  • Authors:
    Lehtinen S, Lees J, Bähler J, Shawe-Taylor J, Orengo C
  • Publication date:
  • Pagination:
    e0134668, ?
  • Journal:
    PloS one
  • Volume:
  • Issue:
  • Medium:
  • Status:
  • Print ISSN:
  • Language:
  • Keywords:
    Proteins, Least-Squares Analysis, Gene Expression Profiling, Algorithms, Information Storage and Retrieval, Databases, Genetic, Gene Regulatory Networks, Gene Ontology
  • Addresses:
    CoMPLEX, University College London, London, United Kingdom; Institute of Structural and Molecular Biology, University College London, London, United Kingdom.
With the growing availability of large-scale biological datasets, automated methods of extracting functionally meaningful information from this data are becoming increasingly important. Data relating to functional association between genes or proteins, such as co-expression or functional association, is often represented in terms of gene or protein networks. Several methods of predicting gene function from these networks have been proposed. However, evaluating the relative performance of these algorithms may not be trivial: concerns have been raised over biases in different benchmarking methods and datasets, particularly relating to non-independence of functional association data and test data. In this paper we propose a new network-based gene function prediction algorithm using a commute-time kernel and partial least squares regression (Compass). We compare Compass to GeneMANIA, a leading network-based prediction algorithm, using a number of different benchmarks, and find that Compass outperforms GeneMANIA on these benchmarks. We also explicitly explore problems associated with the non-independence of functional association data and test data. We find that a benchmark based on the Gene Ontology database, which, directly or indirectly, incorporates information from other databases, may considerably overestimate the performance of algorithms exploiting functional association data for prediction.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers
Genetics, Evolution & Environment
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by