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Random Perturbations of Term Weighted Gene Ontology Annotations for Discovering Gene Unknown Functionalities

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 553))

Abstract

Computational analyses for biomedical knowledge discovery greatly benefit from the availability of the description of gene and protein functional features expressed through controlled terminologies and ontologies, i.e. of their controlled annotations. In the last years, several databases of such annotations have become available; yet, these annotations are incomplete and only some of them represent highly reliable human curated information. To predict and discover unknown or missing annotations existing approaches use unsupervised learning algorithms. We propose a new learning method that allows applying supervised algorithms to unsupervised problems, achieving much better annotation predictions. This method, which we also extend from our preceding work with data weighting techniques, is based on the generation of artificial labeled training sets through random perturbations of original data. We tested it on nine Gene Ontology annotation datasets; obtained results demonstrate that our approach achieves good effectiveness in novel annotation prediction, outperforming state of the art unsupervised methods.

This research is part of the “GenData 2020” project funded by the Italian MIUR.

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Notes

  1. 1.

    http://www.cs.waikato.ac.nz/ml/weka/.

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Author information

Authors and Affiliations

  1. DISI, Università degli Studi di Bologna, Via Venezia 52, 47521, Cesena, Italy

    Giacomo Domeniconi & Gianluca Moro

  2. DEIB, Politecnico di Milano, Piazza L. Da Vinci 32, 20133, Milan, Italy

    Marco Masseroli & Pietro Pinoli

Authors
  1. Giacomo Domeniconi
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  2. Marco Masseroli
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  3. Gianluca Moro
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  4. Pietro Pinoli
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Corresponding author

Correspondence to Giacomo Domeniconi .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisboa, Portugal

    Ana Fred

  2. Delft University of Technology, Delft, Zuid-Holland, The Netherlands

    Jan L. G. Dietz

  3. University of Madeira, Funchal, Portugal

    David Aveiro

  4. Henley Business School, University of Reading, Reading, United Kingdom

    Kecheng Liu

  5. INSTICC, Setubal, Portugal

    Joaquim Filipe

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Domeniconi, G., Masseroli, M., Moro, G., Pinoli, P. (2015). Random Perturbations of Term Weighted Gene Ontology Annotations for Discovering Gene Unknown Functionalities. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2014. Communications in Computer and Information Science, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-319-25840-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-25840-9_12

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  • Online ISBN: 978-3-319-25840-9

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