Using bayesian networks to learn gene regulatory networks

Bernaola Álvarez, Nikolas and Michiels Toquero, Mario and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María (2019). Using bayesian networks to learn gene regulatory networks. In: "3rd HBP Student Conference On Interdisciplinary Brain Research (HBPSC 2019)", 6-7 Feb 2019, Gante, Bélgica. pp. 10-11.

Description

Title: Using bayesian networks to learn gene regulatory networks
Author/s:
  • Bernaola Álvarez, Nikolas
  • Michiels Toquero, Mario
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
Item Type: Presentation at Congress or Conference (Poster)
Event Title: 3rd HBP Student Conference On Interdisciplinary Brain Research (HBPSC 2019)
Event Dates: 6-7 Feb 2019
Event Location: Gante, Bélgica
Title of Book: HBPSC 2019 : 3rd HBP Student Conference On Interdisciplinary Brain Research
Date: 2019
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

We present a variant of the Fast Greedy Equivalence Search algorithm that can be used to learn a Bayesian network that represents the full transcriptional regulatory network of the human brain. We have fully implemented the algorithm and have some preliminary results that show that we can retrieve the Markov Blanket of individual genes of interest with good accuracy and reasonable time (2 hours in a 12 core 2.6GHz computer). The algorithm is parallel and we are currently waiting to deploy it in a supercomputer where we expect to be able to learn the full genome network. This network could then be used as an exploratory tool by the biology community when studying the relationships between genes.

More information

Item ID: 64772
DC Identifier: http://oa.upm.es/64772/
OAI Identifier: oai:oa.upm.es:64772
Deposited by: Memoria Investigacion
Deposited on: 13 Nov 2020 08:25
Last Modified: 13 Nov 2020 08:25
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