Data fusion approach for learning transcriptional Bayesian networks

Elisabetta Sauta, Andrea Demartini, Francesca Vitali, Alberto Riva, Riccardo Bellazzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The complexity of gene expression regulation relies on the synergic nature underlying the molecular interplay among its principal actors, transcription factors (TFs). Exerting a spatiotemporal control on their target genes, they define transcriptional programs across the genome, which are strongly perturbed in a disease context. In order to gain a more comprehensive picture of these complex dynamics, a data fusion approach, aimed at performing the integration of heterogeneous -omics data is fundamental. Bayesian Networks provide a natural framework for integrating different sources of data and knowledge through the priors’ use. In this work, we developed an hybrid structure-learning algorithm with the aim of exploiting TF ChIP-seq and gene expression (GE) data to investigate disease-specific transcriptional regulations in a genome-wide perspective. TF ChIP seq profiles were firstly used for structure learning and then integrated in the model as a prior probability. GE panels were employed to learn the model parameters, trying to find the best heuristic transcriptional network. We applied our approach to a specific pathological case, the chronic myeloid leukemia (CML), a myeloproliferative disorder, whose transcriptional mechanisms have not yet been deeply elucidated. The proposed data-driven method allows to investigate transcriptional signatures, highlighting in the obtained probabilistic network a three-layered hierarchy, as a different TFs influence on gene expression cellular programs.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings
EditorsAnnette [surname]ten Teije, Christian Popow, Lucia Sacchi, John H. Holmes
PublisherSpringer-Verlag
Pages76-80
Number of pages5
ISBN (Print)9783319597577
DOIs
StatePublished - 2017
Externally publishedYes
Event16th Conference on Artificial Intelligence in Medicine, AIME 2017 - Vienna, Austria
Duration: Jun 21 2017Jun 24 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10259 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Conference on Artificial Intelligence in Medicine, AIME 2017
Country/TerritoryAustria
CityVienna
Period6/21/176/24/17

Keywords

  • -omics data integration
  • Bayesian networks
  • Transcriptional regulations

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Data fusion approach for learning transcriptional Bayesian networks'. Together they form a unique fingerprint.

Cite this