Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:6-6, 2017.
Abstract
In the past two decades, the field of proteomics has seen explosive growth, largely due
to the development of tandem mass spectrometry (MS/MS). With a complex biological
sample as input, a typical MS/MS experiment quickly produces a large (often numbering
in the hundreds-of-thousands) collection of spectra representative of the proteins present
in the original complex sample. A majority of widely used methods to search and identify
MS/MS spectra use scoring functions which rely on static, hand-selected parameters rather
than affording the ability to learn parameters and adapt to the widely varying characteristics
of MS/MS data. In this talk, we discuss recent work utilizing dynamic Bayesian networks
(DBNs) to identify MS/MS spectra. In particular, we discuss a recently proposed DBN for
Rapid Identification of Peptides (DRIP) which, in contrast to popular scoring functions,
allows efficient generative and discriminative learning of parameters to achieve state-of-theart
spectrum-identification accuracy. Furthermore, facilitated by DRIP’s generative nature,
we present current innovations leveraging DBNs to significantly enhance many other aspects
of MS/MS analysis, such as improving downstream discriminative classification via detailed
feature extraction and speeding up identification runtime using trellises and approximate
inference.
@InProceedings{pmlr-v73-halloran17a,
title = {Analyzing Tandem Mass Spectra: A Graphical Models Perspective},
author = {John T. Halloran},
booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks},
pages = {6--6},
year = {2017},
editor = {Antti Hyttinen and Joe Suzuki and Brandon Malone},
volume = {73},
series = {Proceedings of Machine Learning Research},
address = {},
month = {20--22 Sep},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v73/halloran17a/halloran17a.pdf},
url = {http://proceedings.mlr.press/v73/halloran17a.html},
abstract = {In the past two decades, the field of proteomics has seen explosive growth, largely due
to the development of tandem mass spectrometry (MS/MS). With a complex biological
sample as input, a typical MS/MS experiment quickly produces a large (often numbering
in the hundreds-of-thousands) collection of spectra representative of the proteins present
in the original complex sample. A majority of widely used methods to search and identify
MS/MS spectra use scoring functions which rely on static, hand-selected parameters rather
than affording the ability to learn parameters and adapt to the widely varying characteristics
of MS/MS data. In this talk, we discuss recent work utilizing dynamic Bayesian networks
(DBNs) to identify MS/MS spectra. In particular, we discuss a recently proposed DBN for
Rapid Identification of Peptides (DRIP) which, in contrast to popular scoring functions,
allows efficient generative and discriminative learning of parameters to achieve state-of-theart
spectrum-identification accuracy. Furthermore, facilitated by DRIP’s generative nature,
we present current innovations leveraging DBNs to significantly enhance many other aspects
of MS/MS analysis, such as improving downstream discriminative classification via detailed
feature extraction and speeding up identification runtime using trellises and approximate
inference.
}
}
%0 Conference Paper
%T Analyzing Tandem Mass Spectra: A Graphical Models Perspective
%A John T. Halloran
%B Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks
%C Proceedings of Machine Learning Research
%D 2017
%E Antti Hyttinen
%E Joe Suzuki
%E Brandon Malone
%F pmlr-v73-halloran17a
%I PMLR
%J Proceedings of Machine Learning Research
%P 6--6
%U http://proceedings.mlr.press
%V 73
%W PMLR
%X In the past two decades, the field of proteomics has seen explosive growth, largely due
to the development of tandem mass spectrometry (MS/MS). With a complex biological
sample as input, a typical MS/MS experiment quickly produces a large (often numbering
in the hundreds-of-thousands) collection of spectra representative of the proteins present
in the original complex sample. A majority of widely used methods to search and identify
MS/MS spectra use scoring functions which rely on static, hand-selected parameters rather
than affording the ability to learn parameters and adapt to the widely varying characteristics
of MS/MS data. In this talk, we discuss recent work utilizing dynamic Bayesian networks
(DBNs) to identify MS/MS spectra. In particular, we discuss a recently proposed DBN for
Rapid Identification of Peptides (DRIP) which, in contrast to popular scoring functions,
allows efficient generative and discriminative learning of parameters to achieve state-of-theart
spectrum-identification accuracy. Furthermore, facilitated by DRIP’s generative nature,
we present current innovations leveraging DBNs to significantly enhance many other aspects
of MS/MS analysis, such as improving downstream discriminative classification via detailed
feature extraction and speeding up identification runtime using trellises and approximate
inference.
Halloran, J.T.. (2017). Analyzing Tandem Mass Spectra: A Graphical Models Perspective. Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, in PMLR 73:6-6
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