FABIA: Factor Analysis for Bicluster Acquisition

Sepp Hochreiter; Ulrich Bodenhofer; Martin Heusel; Andreas Mayr; Andreas Mitterecker; Adetayo Kasim; Tatsiana Khamiakova; Suzy Van Sanden; Dan Lin; Willem Talloen; Luc Bijnens; Hinrich W.H. Göhlmann; Ziv Shkedy; Djork-Arné Clevert

FABIA: Factor Analysis for Bicluster Acquisition

Sepp Hochreiter
, Ulrich Bodenhofer
, Martin Heusel
, Andreas Mayr
, Andreas Mitterecker
, Adetayo Kasim
, Tatsiana Khamiakova
, Suzy Van Sanden
, Dan Lin
, Willem Talloen
, Luc Bijnens
, Hinrich W.H. Göhlmann
, Ziv Shkedy
, Djork-Arné Clevert

Institut für Machine Learning

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

Abstract

Biclustering genes and samples is important to extract knowledge from gene expression measurements. Current methods are not generative models allowing model selection and Bayesian framework or are additive models not explaining mRNA effects and PCR amplification. We introduce a generative, multiplicative model which assumes realistic non-Gaussian heavy tail distributions.

Originalsprache	Englisch
Titel	ISMB 2010 Proceedings
Seitenumfang	1
Publikationsstatus	Veröffentlicht - Juli 2010

Wissenschaftszweige

101004 Biomathematik
101027 Dynamische Systeme
101028 Mathematische Modellierung
101029 Mathematische Statistik
101014 Numerische Mathematik
101015 Operations Research
101016 Optimierung
101017 Spieltheorie
101018 Statistik
101019 Stochastik
101024 Wahrscheinlichkeitstheorie
101026 Zeitreihenanalyse
102 Informatik
102001 Artificial Intelligence
102003 Bildverarbeitung
102004 Bioinformatik
102013 Human-Computer Interaction
102018 Künstliche Neuronale Netze
102019 Machine Learning
103029 Statistische Physik
106005 Bioinformatik
106007 Biostatistik
202017 Embedded Systems
202035 Robotik
202036 Sensorik
202037 Signalverarbeitung
305901 Computerunterstützte Diagnose und Therapie
305905 Medizinische Informatik
305907 Medizinische Statistik
102032 Computational Intelligence
102033 Data Mining
101031 Approximationstheorie

Andere Dateien und Links

Publikationen Bioinformatik

Dieses zitieren

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title = "FABIA: Factor Analysis for Bicluster Acquisition",

abstract = "Biclustering genes and samples is important to extract knowledge from gene expression measurements. Current methods are not generative models allowing model selection and Bayesian framework or are additive models not explaining mRNA effects and PCR amplification. We introduce a generative, multiplicative model which assumes realistic non-Gaussian heavy tail distributions.",

author = "Sepp Hochreiter and Ulrich Bodenhofer and Martin Heusel and Andreas Mayr and Andreas Mitterecker and Adetayo Kasim and Tatsiana Khamiakova and \{Van Sanden\}, Suzy and Dan Lin and Willem Talloen and Luc Bijnens and G{\"o}hlmann, \{Hinrich W.H.\} and Ziv Shkedy and Djork-Arn{\'e} Clevert",

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AU - Hochreiter, Sepp

AU - Bodenhofer, Ulrich

AU - Heusel, Martin

AU - Mayr, Andreas

AU - Mitterecker, Andreas

AU - Kasim, Adetayo

AU - Khamiakova, Tatsiana

AU - Van Sanden, Suzy

AU - Lin, Dan

AU - Talloen, Willem

AU - Bijnens, Luc

AU - Göhlmann, Hinrich W.H.

AU - Shkedy, Ziv

AU - Clevert, Djork-Arné

PY - 2010/7

Y1 - 2010/7

N2 - Biclustering genes and samples is important to extract knowledge from gene expression measurements. Current methods are not generative models allowing model selection and Bayesian framework or are additive models not explaining mRNA effects and PCR amplification. We introduce a generative, multiplicative model which assumes realistic non-Gaussian heavy tail distributions.

AB - Biclustering genes and samples is important to extract knowledge from gene expression measurements. Current methods are not generative models allowing model selection and Bayesian framework or are additive models not explaining mRNA effects and PCR amplification. We introduce a generative, multiplicative model which assumes realistic non-Gaussian heavy tail distributions.

UR - http://www.bioinf.jku.at/publications/bioinf/2010.html

M3 - Conference proceedings

BT - ISMB 2010 Proceedings

ER -