TY - GEN
T1 - Evaluation of label dependency for the prediction of HLA genes
AU - Paunić, Vanja
AU - Steinbach, Michael
AU - Madbouly, Abeer
AU - Kumar, Vipin
PY - 2013
Y1 - 2013
N2 - 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 inammatory 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.
AB - 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 inammatory 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.
KW - HLA imputation
KW - Human leukocyte antigen
KW - Label dependency
KW - Multi-label prediction
KW - SNPs
UR - http://www.scopus.com/inward/record.url?scp=84888140690&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84888140690&partnerID=8YFLogxK
U2 - 10.1145/2506583.2506632
DO - 10.1145/2506583.2506632
M3 - Conference contribution
AN - SCOPUS:84888140690
SN - 9781450324342
T3 - 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
SP - 296
EP - 305
BT - 2013 ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
T2 - 2013 4th ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, ACM-BCB 2013
Y2 - 22 September 2013 through 25 September 2013
ER -