Learning interpretable Gene Regulatory Networks via merging Bayesian networks

Bernaola Álvarez, Nikolas (2019). Learning interpretable Gene Regulatory Networks via merging Bayesian networks. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Learning interpretable Gene Regulatory Networks via merging Bayesian networks
Author/s:
  • Bernaola Álvarez, Nikolas
Contributor/s:
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro
Item Type: Thesis (Master thesis)
Masters title: Inteligencia Artificial
Date: July 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

Nuestro trabajo empieza con la necesidad de reconstruir una red de regulacion genética para el genoma humano usando datos del cerebro. Para conseguirlo, estudiamos el problema biológico de como aprender una red de regulación genética y revisamos la literatura para ver cuales son los metodos mas populares para resolver este problema, junto con sus ventajas y limitaciones. Al final, decidimos que el metodo que mejor se ajusta a nuestras necesidades son las redes bayesianas, sobre todo por su interpretabilidad. En este trabajo presentamos un nuevo algoritmo, FGESMerge, capaz de aprender la estructura de una red de regulación genética mediante la unión de varias redes bayesianas aprendidas localmente alrededor de cada uno de los genes, utilizando una variante del Fast Greedy Equivalence Search (FGES). El método es competitivo con el estado del arte en su capacidad de recuperar la estructura original y ademas es mucho más rápido y escala a decenas de miles de variables. Tambien presentamos una solución al problema de inferencia para redes bayesianas con miles de variables. FGES-Merge y la herramienta de inferencia estan disponibles publicamente en Neurosuites y pueden ser utilizadas por la comunidad de biología para guiar su investigación hacia las interacciones entre genes que nuestro modelo predice.---ABSTRACT---Our work was motivated by the need of learning a genome-wide regulatory network for the human brain from the Allen Human Brain Atlas dataset. To achieve this, we studied the biological problem and we reviewed the literature for different methods for learning gene regulatory networks, noting their advantages and limitations. We decided to use Bayesian networks because of their interpretability and so we present a new method for learning the structure of gene regulatory networks via merging of locally learned Bayesian networks, based on the Fast Greedy Equivalent Search algorithm. The method is competitive with the state of the art in recall of the true structure while also being much faster and scaling up to the tens of thousands of variables. We also solved the problem of inference for large numbers of variables in Bayesian networks. Both the structure learning algorithm and the inference tool are available publicly at Neurosuites and can be used to guide biological research by testing new gene interactions that are predicted with high confidence by the model.

More information

Item ID: 55996
DC Identifier: http://oa.upm.es/55996/
OAI Identifier: oai:oa.upm.es:55996
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 06 Aug 2019 08:42
Last Modified: 06 Aug 2019 08:42
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