Using Rule-Based Machine Learning for Candidate Disease Gene Prioritization and Sample Classification of Cancer Gene Expression Data

Enrico Glaab; Jaume Bacardit; Jonathan M. Garibaldi; Natalio Krasnogor

doi:10.1371/journal.pone.0039932

Loading metrics

Open Access

Peer-reviewed

Research Article

Using Rule-Based Machine Learning for Candidate Disease Gene Prioritization and Sample Classification of Cancer Gene Expression Data

Enrico Glaab,

Current address: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

Affiliation Interdisciplinary Computing and Complex Systems (ICOS) Research Group, University of Nottingham, Nottingham, United Kingdom
⨯
Jaume Bacardit,

Affiliation Interdisciplinary Computing and Complex Systems (ICOS) Research Group, University of Nottingham, Nottingham, United Kingdom
⨯
Jonathan M. Garibaldi,

Affiliation Intelligent Modeling and Analysis (IMA) Research Group, University of Nottingham, Nottingham, United Kingdom
⨯
Natalio Krasnogor

* E-mail: Natalio.Krasnogor@Nottingham.ac.uk

Affiliation Interdisciplinary Computing and Complex Systems (ICOS) Research Group, University of Nottingham, Nottingham, United Kingdom
⨯

Using Rule-Based Machine Learning for Candidate Disease Gene Prioritization and Sample Classification of Cancer Gene Expression Data

Enrico Glaab,
Jaume Bacardit,
Jonathan M. Garibaldi,
Natalio Krasnogor

Published: July 11, 2012
https://doi.org/10.1371/journal.pone.0039932

About the Authors

Enrico Glaab

Current address: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

Affiliation Interdisciplinary Computing and Complex Systems (ICOS) Research Group, University of Nottingham, Nottingham, United Kingdom

Jaume Bacardit

Affiliation Interdisciplinary Computing and Complex Systems (ICOS) Research Group, University of Nottingham, Nottingham, United Kingdom

Jonathan M. Garibaldi

Affiliation Intelligent Modeling and Analysis (IMA) Research Group, University of Nottingham, Nottingham, United Kingdom

Natalio Krasnogor

* E-mail: Natalio.Krasnogor@Nottingham.ac.uk

Affiliation Interdisciplinary Computing and Complex Systems (ICOS) Research Group, University of Nottingham, Nottingham, United Kingdom

Competing Interests

The authors have declared that no competing interests exist.

Author Contributions

Conceived and designed the experiments: EG JB NK. Performed the experiments: EG. Analyzed the data: EG JB JMG NK. Wrote the paper: EG. Conceived main algorithms (GAssist, BioHEL): JB NK Revised manuscript critically for important intellectual content: EG JB JMG NK Funding: JMG NK Final approval of the version to be published: EG JB JMG NK.

Subject Areas
?

For more information about PLOS Subject Areas, click here.
We want your feedback. Do these Subject Areas make sense for this article? Click the target next to the incorrect Subject Area and let us know. Thanks for your help!

Breast cancer
Is the Subject Area "Breast cancer" applicable to this article?

Thanks for your feedback.
Prostate cancer
Is the Subject Area "Prostate cancer" applicable to this article?

Thanks for your feedback.
Microarrays
Is the Subject Area "Microarrays" applicable to this article?

Thanks for your feedback.
Machine learning
Is the Subject Area "Machine learning" applicable to this article?

Thanks for your feedback.
Data mining
Is the Subject Area "Data mining" applicable to this article?

Thanks for your feedback.
Lymphoma
Is the Subject Area "Lymphoma" applicable to this article?

Thanks for your feedback.
Forecasting
Is the Subject Area "Forecasting" applicable to this article?

Thanks for your feedback.
Machine learning algorithms
Is the Subject Area "Machine learning algorithms" applicable to this article?

Thanks for your feedback.