Temporal ordering of cancer microarray data through a reinforcement learning based approach.

Temporal modeling and analysis and more specifically, temporal ordering are very important problems within the fields of bioinformatics and computational biology, as the temporal analysis of the events characterizing a certain biological process could provide significant insights into its developmen...

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Autores principales:	Gabriela Czibula, Iuliana M Bocicor, Istvan-Gergely Czibula
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8236f459dbb8431690fd4774fc17620c
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id	oai:doaj.org-article:8236f459dbb8431690fd4774fc17620c
record_format	dspace
spelling	oai:doaj.org-article:8236f459dbb8431690fd4774fc17620c2021-11-18T07:50:53ZTemporal ordering of cancer microarray data through a reinforcement learning based approach.1932-620310.1371/journal.pone.0060883https://doaj.org/article/8236f459dbb8431690fd4774fc17620c2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23565283/?tool=EBIhttps://doaj.org/toc/1932-6203Temporal modeling and analysis and more specifically, temporal ordering are very important problems within the fields of bioinformatics and computational biology, as the temporal analysis of the events characterizing a certain biological process could provide significant insights into its development and progression. Particularly, in the case of cancer, understanding the dynamics and the evolution of this disease could lead to better methods for prediction and treatment. In this paper we tackle, from a computational perspective, the temporal ordering problem, which refers to constructing a sorted collection of multi-dimensional biological data, collection that reflects an accurate temporal evolution of biological systems. We introduce a novel approach, based on reinforcement learning, more precisely, on Q-learning, for the biological temporal ordering problem. The experimental evaluation is performed using several DNA microarray data sets, two of which contain cancer gene expression data. The obtained solutions are correlated either to the given correct ordering (in the cases where this is provided for validation), or to the overall survival time of the patients (in the case of the cancer data sets), thus confirming a good performance of the proposed model and indicating the potential of our proposal.Gabriela CzibulaIuliana M BocicorIstvan-Gergely CzibulaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 4, p e60883 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gabriela Czibula Iuliana M Bocicor Istvan-Gergely Czibula Temporal ordering of cancer microarray data through a reinforcement learning based approach.
description	Temporal modeling and analysis and more specifically, temporal ordering are very important problems within the fields of bioinformatics and computational biology, as the temporal analysis of the events characterizing a certain biological process could provide significant insights into its development and progression. Particularly, in the case of cancer, understanding the dynamics and the evolution of this disease could lead to better methods for prediction and treatment. In this paper we tackle, from a computational perspective, the temporal ordering problem, which refers to constructing a sorted collection of multi-dimensional biological data, collection that reflects an accurate temporal evolution of biological systems. We introduce a novel approach, based on reinforcement learning, more precisely, on Q-learning, for the biological temporal ordering problem. The experimental evaluation is performed using several DNA microarray data sets, two of which contain cancer gene expression data. The obtained solutions are correlated either to the given correct ordering (in the cases where this is provided for validation), or to the overall survival time of the patients (in the case of the cancer data sets), thus confirming a good performance of the proposed model and indicating the potential of our proposal.
format	article
author	Gabriela Czibula Iuliana M Bocicor Istvan-Gergely Czibula
author_facet	Gabriela Czibula Iuliana M Bocicor Istvan-Gergely Czibula
author_sort	Gabriela Czibula
title	Temporal ordering of cancer microarray data through a reinforcement learning based approach.
title_short	Temporal ordering of cancer microarray data through a reinforcement learning based approach.
title_full	Temporal ordering of cancer microarray data through a reinforcement learning based approach.
title_fullStr	Temporal ordering of cancer microarray data through a reinforcement learning based approach.
title_full_unstemmed	Temporal ordering of cancer microarray data through a reinforcement learning based approach.
title_sort	temporal ordering of cancer microarray data through a reinforcement learning based approach.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/8236f459dbb8431690fd4774fc17620c
work_keys_str_mv	AT gabrielaczibula temporalorderingofcancermicroarraydatathroughareinforcementlearningbasedapproach AT iulianambocicor temporalorderingofcancermicroarraydatathroughareinforcementlearningbasedapproach AT istvangergelyczibula temporalorderingofcancermicroarraydatathroughareinforcementlearningbasedapproach
_version_	1718422864004120576

Temporal ordering of cancer microarray data through a reinforcement learning based approach.

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