Ranking of gene regulators through differential equations and Gaussian processes

Antti Honkela; Marta Milo; Matthew Holley; Magnus Rattray; Neil D. Lawrence

doi:10.1109/MLSP.2010.5589258

Ranking of gene regulators through differential equations and Gaussian processes

Antti Honkela, Marta Milo, Matthew Holley, Magnus Rattray, Neil D. Lawrence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Gene regulation is controlled by transcription factor proteins which themselves are encoded as genes. This gives a network of interacting genes which control the functioning of a cell. With the advent of genome wide expression measurements the targets of given transcription factor have been sought through techniques such as clustering. In this paper we consider the harder problem of finding a gene's regulator instead of its targets. We use a model-based differential equation approach combined with a Gaussian process prior distribution for unobserved continuous-time regulator expression profile. Candidate regulators can then be ranked according to model likelihood. This idea, that we refer to as ranked regulator prediction (RRP), is then applied to finding the regulators of Gata3, an important developmental transcription factor, in the development of ear hair cells. ©2010 IEEE.

Original language	English
Title of host publication	Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010\|Proc. IEEE Int. Workshop Mach. Learn. Signal Process., MLSP
Pages	154-159
Number of pages	5
DOIs	https://doi.org/10.1109/MLSP.2010.5589258
Publication status	Published - 2010
Event	2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 - Kittila Duration: 1 Jul 2010 → …

Conference

Conference	2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010
City	Kittila
Period	1/07/10 → …

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10.1109/MLSP.2010.5589258

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Honkela, A., Milo, M., Holley, M., Rattray, M., & Lawrence, N. D. (2010). Ranking of gene regulators through differential equations and Gaussian processes. In Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010|Proc. IEEE Int. Workshop Mach. Learn. Signal Process., MLSP (pp. 154-159) https://doi.org/10.1109/MLSP.2010.5589258

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title = "Ranking of gene regulators through differential equations and Gaussian processes",

abstract = "Gene regulation is controlled by transcription factor proteins which themselves are encoded as genes. This gives a network of interacting genes which control the functioning of a cell. With the advent of genome wide expression measurements the targets of given transcription factor have been sought through techniques such as clustering. In this paper we consider the harder problem of finding a gene's regulator instead of its targets. We use a model-based differential equation approach combined with a Gaussian process prior distribution for unobserved continuous-time regulator expression profile. Candidate regulators can then be ranked according to model likelihood. This idea, that we refer to as ranked regulator prediction (RRP), is then applied to finding the regulators of Gata3, an important developmental transcription factor, in the development of ear hair cells. {\textcopyright}2010 IEEE.",

author = "Antti Honkela and Marta Milo and Matthew Holley and Magnus Rattray and Lawrence, {Neil D.}",

year = "2010",

doi = "10.1109/MLSP.2010.5589258",

language = "English",

isbn = "9781424478774",

pages = "154--159",

booktitle = "Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010|Proc. IEEE Int. Workshop Mach. Learn. Signal Process., MLSP",

note = "2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 ; Conference date: 01-07-2010",

}

Honkela, A, Milo, M, Holley, M, Rattray, M & Lawrence, ND 2010, Ranking of gene regulators through differential equations and Gaussian processes. in Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010|Proc. IEEE Int. Workshop Mach. Learn. Signal Process., MLSP. pp. 154-159, 2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010, Kittila, 1/07/10. https://doi.org/10.1109/MLSP.2010.5589258

Ranking of gene regulators through differential equations and Gaussian processes. / Honkela, Antti; Milo, Marta; Holley, Matthew et al.

Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010|Proc. IEEE Int. Workshop Mach. Learn. Signal Process., MLSP. 2010. p. 154-159.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Ranking of gene regulators through differential equations and Gaussian processes

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AU - Milo, Marta

AU - Holley, Matthew

AU - Rattray, Magnus

AU - Lawrence, Neil D.

PY - 2010

Y1 - 2010

N2 - Gene regulation is controlled by transcription factor proteins which themselves are encoded as genes. This gives a network of interacting genes which control the functioning of a cell. With the advent of genome wide expression measurements the targets of given transcription factor have been sought through techniques such as clustering. In this paper we consider the harder problem of finding a gene's regulator instead of its targets. We use a model-based differential equation approach combined with a Gaussian process prior distribution for unobserved continuous-time regulator expression profile. Candidate regulators can then be ranked according to model likelihood. This idea, that we refer to as ranked regulator prediction (RRP), is then applied to finding the regulators of Gata3, an important developmental transcription factor, in the development of ear hair cells. ©2010 IEEE.

AB - Gene regulation is controlled by transcription factor proteins which themselves are encoded as genes. This gives a network of interacting genes which control the functioning of a cell. With the advent of genome wide expression measurements the targets of given transcription factor have been sought through techniques such as clustering. In this paper we consider the harder problem of finding a gene's regulator instead of its targets. We use a model-based differential equation approach combined with a Gaussian process prior distribution for unobserved continuous-time regulator expression profile. Candidate regulators can then be ranked according to model likelihood. This idea, that we refer to as ranked regulator prediction (RRP), is then applied to finding the regulators of Gata3, an important developmental transcription factor, in the development of ear hair cells. ©2010 IEEE.

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DO - 10.1109/MLSP.2010.5589258

M3 - Conference contribution

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BT - Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010|Proc. IEEE Int. Workshop Mach. Learn. Signal Process., MLSP

T2 - 2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010

Y2 - 1 July 2010

ER -

Ranking of gene regulators through differential equations and Gaussian processes

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