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Bayesian Methods in Bioinformatics

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 1773

Special Issue Editor

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Guest Editor
Department of Measurement and Information Systems, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest 1111, Hungary
Interests: artificial intelligence; machine learning; Bayesian approaches; probabilistic graphical models; causality research; chemoinformatics; bioinformatics

Special Issue Information

Dear Colleagues,

Bayesian methods and bioinformatics have a long-standing, mutually inspiring relationship. On one hand, the Bayesian approach has allowed for the development of cutting-edge bioinformatics solutions, including precision sequencing, genotypic data imputation, phylogenetic analysis, information fusion for drug, gene, and variant prioritization, genome-wide association studies (GWASes), distillation of multimorbidity dependency maps, and de novo molecule generation. On the other hand, novel bioinformatics data sets and problems motivate further developments of Bayesian methods, such as federated biobanks with hierarchical clinical data and heterogeneous omics data; temporal electronic health records with multiple resolutions, irregularly sampled, self-quantified data; heterogeneous, large-scale drug-target interaction data, increasingly including natural products beyond small synthetic compounds, and protein/nucleotide drug candidates; omics-wide summary statistics for associations and causal effects for the entire phenome; and cross-domain, cross-species semantic linked open data (LOD) with more and more quantitative uncertainty information.

In addition to the rich variety of Bayesian bioinformatics results, the Bayesian approach also provides a consistent and overarching framework for the whole scientific lifecycle: quantifying the value of scientific data; designing experiments; coping with noisy and incomplete data; combining multiple data sets, partial statistics and knowledge fragments; model averaging; and reporting full posteriors. Significantly, the Bayesian approach allows for the construction of a novel, intermediate layer of scientific knowledge between data and interpreted scientific conclusions via the systematic reporting of posteriors. Large-scale inference using such kinds of omics-wide statistics has already led to significant discoveries, e.g., causal inference using GWAS summary statistics.

This Special Issue invites papers that advance computational developments of Bayesian bioinformatics with particular emphasis on Bayesian publishing, i.e., reporting posteriors systematically and semantically as an intermediate quantitative layer of scientific knowledge (see semantic publishing, linked data). Submissions that discuss the generation, sharing, and combination of posteriors linking multiple phases of data analysis, different domains, species, and abstraction hierarchy levels are especially welcome. Papers that concern artificial intelligence and active learning methods autonomously using Bayesian linked data for automated scientific discovery are also encouraged.

Dr. Peter Antal
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • data and knowledge fusion
  • approximate Bayesian computation
  • probabilistic graphical models
  • summary statistics
  • linked open data
  • federated learning
  • active learning
  • causal inference
  • design of experiments
  • machine science
  • discovery systems
  • drug discovery
  • bioinformatics

Published Papers (1 paper)

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14 pages, 416 KiB  
Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks
by Xiaohan Wei, Yulai Zhang and Cheng Wang
Entropy 2022, 24(10), 1351; https://doi.org/10.3390/e24101351 - 24 Sep 2022
Cited by 2 | Viewed by 1337
Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in [...] Read more.
Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly. Full article
(This article belongs to the Special Issue Bayesian Methods in Bioinformatics)
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