Paper Info
Title
Random Perturbations of Term Weighted Gene Ontology Annotations for Discovering Gene Unknown Functionalities.
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.
Year
DOI
Venue
2014
10.1007/978-3-319-25840-9_12
Communications in Computer and Information Science
Keywords
Field
DocType
Gene ontology,Biomolecular annotation prediction,Bioinformatics,Knowledge discovery,Supervised learning,Term weighting
Ontology (information science),Data mining,Weighting,Annotation,Computer science,Gene ontology,Gene ontology annotation,Supervised learning,Unsupervised learning,Knowledge extraction
Conference
Volume
ISSN
Citations 
553
1865-0929
2
PageRank 
References 
Authors
0.35
20
4
Name
Order
Citations
PageRank
Giacomo Domeniconi161.07
Marco Masseroli240248.55
G. Moro319216.25
Pietro Pinoli410417.13