Article

Active learning for sampling in time-series experiments with application to gene expression analysis

Authors:

Ziv Bar-JosephAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

Pages 832 - 839

https://doi.org/10.1145/1102351.1102456

Published: 07 August 2005 Publication History

Abstract

Many time-series experiments seek to estimate some signal as a continuous function of time. In this paper, we address the sampling problem for such experiments: determining which time-points ought to be sampled in order to minimize the cost of data collection. We restrict our attention to a growing class of experiments which measure multiple signals at each time-point and where raw materials/observations are archived initially, and selectively analyzed later, this analysis being the more expensive step. We present an active learning algorithm for iteratively choosing time-points to sample, using the uncertainty in the quality of the currently estimated time-dependent curve as the objective function. Using simulated data as well as gene expression data, we show that our algorithm performs well, and can significantly reduce experimental cost without loss of information.

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Cited By

Jarl SAronsson LRahrovani SChehreghani M(2022)Active learning of driving scenario trajectoriesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.104972113(104972)Online publication date: Aug-2022
https://doi.org/10.1016/j.engappai.2022.104972
Ezer DKeir J(2019)NITPicker: selecting time points for follow-up experimentsBMC Bioinformatics10.1186/s12859-019-2717-520:1Online publication date: 2-Apr-2019
https://doi.org/10.1186/s12859-019-2717-5
Ramakrishnan NTadepalli SWatson LHelm RAntoniotti MMishra B(2010)Reverse engineering dynamic temporal models of biological processes and their relationshipsProceedings of the National Academy of Sciences10.1073/pnas.1006283107107:28(12511-12516)Online publication date: 22-Jun-2010
https://doi.org/10.1073/pnas.1006283107
Show More Cited By

Active learning for sampling in time-series experiments with application to gene expression analysis
1. Applied computing
  1. Life and medical sciences

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Published In

cover image ACM Other conferences

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 07 August 2005

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Cited By

Jarl SAronsson LRahrovani SChehreghani M(2022)Active learning of driving scenario trajectoriesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.104972113(104972)Online publication date: Aug-2022
https://doi.org/10.1016/j.engappai.2022.104972
Ezer DKeir J(2019)NITPicker: selecting time points for follow-up experimentsBMC Bioinformatics10.1186/s12859-019-2717-520:1Online publication date: 2-Apr-2019
https://doi.org/10.1186/s12859-019-2717-5
Ramakrishnan NTadepalli SWatson LHelm RAntoniotti MMishra B(2010)Reverse engineering dynamic temporal models of biological processes and their relationshipsProceedings of the National Academy of Sciences10.1073/pnas.1006283107107:28(12511-12516)Online publication date: 22-Jun-2010
https://doi.org/10.1073/pnas.1006283107
Jammalamadaka AGhosh K(2010)A Semiparametric Bayesian Method of Clustering Genes Using Time-Series of Expression ProfilesAdvances in Directional and Linear Statistics10.1007/978-3-7908-2628-9_6(85-96)Online publication date: 27-Sep-2010
https://doi.org/10.1007/978-3-7908-2628-9_6

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