Evaluation of Label Dependency for the Prediction of HLA Genes

Authors:
Vanja Paunić;Michael Steinbach;Abeer Madbouly;Vipin Kumar
Affiliations:
Dept. of Computer Science, University of Minnesota, Minneapolis, MN, 55455;Dept. of Computer Science, University of Minnesota, Minneapolis, MN, 55455;Bioinformatics Research, National Marrow Donor Program, Minneapolis, MN, 55413;Dept. of Computer Science, University of Minnesota, Minneapolis, MN, 55455
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

Citing 8
Cited 0

Collective multi-label classification

Proceedings of the 14th ACM international conference on Information and knowledge management
Information theory applied to the sparse gene ontology annotation network to predict novel gene function

Bioinformatics
Random k-Labelsets: An Ensemble Method for Multilabel Classification

ECML '07 Proceedings of the 18th European conference on Machine Learning
Classifier Chains for Multi-label Classification

ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
A simple approach to incorporate label dependency in multi-label classification

MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Multi-label correlated semi-supervised learning for protein function prediction

ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
HLA type inference via haplotypes identical by descent

RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Prediction of HLA Genes from SNP Data and HLA Haplotype Frequencies

ICDMW '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining Workshops

Quantified Score

Hi-index	0.00

Visualization

Abstract

The Human Leukocyte Antigen (HLA) gene system plays a crucial role in hematopoietic stem cell transplantation, where patients and donors are matched with respect to their HLA genes in order to maximize the chances of a successful transplant. It is the most polymorphic region of the human genome with some of the strongest associations with autoimmune, infectious, and inflammatory diseases. The availability of HLA data is, therefore, of high importance to clinicians and researchers. However, due to its high polymorphism, obtaining it is time- and cost-prohibitive. We previously described a method for the prediction of HLA genes from widely available Single Nucleotide Polymorphism (SNP) data. In this paper we show that using HLA gene dependency information improves prediction performance on multiple real-world data sets. More specifically, we propose and evaluate different approaches for integrating HLA gene dependency into the prediction process. The results from experiments on two real data sets show that adding dependency information is a valuable asset for HLA gene prediction, particularly for smaller data sets.