@inproceedings{4b0017fa4db548518d80ebe8311a962b,
title = "PeakSeg: Constrained optimal segmentation and supervised penalty learning for peak detection in count data",
abstract = "Peak detection is a central problem in genomic data analysis, and current algorithms for this task are unsupervised and mostly effective for a single data type and pattern (e.g. H3K4me3 data with a sharp peak pattern). We propose PeakSeg, a new constrained maximum likelihood segmentation model for peak detection with an efficient inference algorithm: constrained dynamic programming. We investigate unsupervised and supervised learning of penalties for the critical model selection problem. We show that the supervised method has state-of-the-art peak detection across all data sets in a benchmark that includes both sharp H3K4me3 and broad H3K36me3 patterns.",
author = "Hocking, {Toby Dylan} and Guillem Rigaill and Guillaume Bourque",
year = "2015",
language = "English (US)",
series = "32nd International Conference on Machine Learning, ICML 2015",
publisher = "International Machine Learning Society (IMLS)",
pages = "324--332",
editor = "Francis Bach and David Blei",
booktitle = "32nd International Conference on Machine Learning, ICML 2015",
note = "32nd International Conference on Machine Learning, ICML 2015 ; Conference date: 06-07-2015 Through 11-07-2015",
}