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Algorithms
Special Issues
Biological Networks II
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Biological Networks II

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 3546

Special Issue Editor

Prof. Dr. Tatsuya Akutsu

E-Mail Website
Guest Editor
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan
Interests: computational biology; string and tree algorithms; complex networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In bioinformatics, the behaviors of cells are often mathematically modeled by various kinds of biological networks, including gene regulatory networks, metabolic networks, protein–protein interaction networks, signal networks, transcription networks, phylogenetic networks, etc.
The main themes of this Special Issue (though not an exhaustive list) are algorithms for biological networks, which include discrete algorithms, statistical algorithms, heuristic algorithms, probabilistic algorithms, randomized algorithms, and machine learning methods to solve problems in biological networks that are computationally efficient. We accept both review papers and research papers.

Dr. Tatsuya Akutsu
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. Algorithms 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 1600 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.

Keywords

  • Protein–protein interaction networks
  • Genetic networks
  • Metabolic networks
  • Boolean networks
  • Bayesian networks
  • Petri nets
  • Discrete algorithms
  • Machine learning methods

Published Papers (1 paper)

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Research

9 pages, 228 KiB  
Article
Construction Method of Probabilistic Boolean Networks Based on Imperfect Information
by Katsuaki Umiji, Koichi Kobayashi and Yuh Yamashita
Algorithms 2019, 12(12), 268; https://0-doi-org.brum.beds.ac.uk/10.3390/a12120268 - 12 Dec 2019
Cited by 2 | Viewed by 3075
Abstract
A probabilistic Boolean network (PBN) is well known as one of the mathematical models of gene regulatory networks. In a Boolean network, expression of a gene is approximated by a binary value, and its time evolution is expressed by Boolean functions. In a [...] Read more.
A probabilistic Boolean network (PBN) is well known as one of the mathematical models of gene regulatory networks. In a Boolean network, expression of a gene is approximated by a binary value, and its time evolution is expressed by Boolean functions. In a PBN, a Boolean function is probabilistically chosen from candidates of Boolean functions. One of the authors has proposed a method to construct a PBN from imperfect information. However, there is a weakness that the number of candidates of Boolean functions may be redundant. In this paper, this construction method is improved to efficiently utilize given information. To derive Boolean functions and those selection probabilities, the linear programming problem is solved. Here, we introduce the objective function to reduce the number of candidates. The proposed method is demonstrated by a numerical example. Full article
(This article belongs to the Special Issue Biological Networks II)
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