@inproceedings{fb8bb354acdf4ca891a3732aa8e5ca55,
title = "Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines",
abstract = "We present a statistical framework, hierarchical relevance vector machine (H-RVM), for improved prediction of scalar outcomes using interacting high-dimensional input covariates from different sources. We illustrate our methodology for integrating genomic data from multiple platforms to predict observed clinical phenotypes. H-RVM is a hierarchical Bayesian generalization of the relevance vector machine and its learning algorithm is a special case of the computationally efficient variational method of hierarchic kernel learning frame-work. We apply H-RVM to data from the Cancer Genome Atlas based Glioblastoma study to predict imaging-based tumor volume by integrating gene and miRNA expression data and show that H-RVM performs much better in prediction as compared to competing methods.",
keywords = "Bayesian modeling, genomics, high-dimensional data analysis, multiple kernel learning, prediction",
author = "Sanvesh Srivastava and Wenyi Wang and Zinn, {Pascal O.} and Colen, {Rivka R.} and Veerabhadran Baladandayuthapani",
year = "2012",
doi = "10.1109/GENSIPS.2012.6507716",
language = "English (US)",
isbn = "9781467352369",
series = "Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics",
pages = "18--21",
booktitle = "Proceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012",
note = "2012 IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2012 ; Conference date: 02-12-2012 Through 04-12-2012",
}