Special Issue "Bioinformatics Algorithms and Applications"

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

Deadline for manuscript submissions: closed (1 December 2017)

Special Issue Editor

Guest Editor
Dr. Max Alekseyev

Computational Biology Institute & Department of Mathematics, George Washington University, Ashburn, VA, USA
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Special Issue Information

Dear Colleagues,

The conference «Bioinformatics: from Algorithms to Applications» will be held from August 1–3, 2017, at Saint Petersburg State University, Saint Petersburg, Russia.

Modern life science studies involve both biological and computational methods. Breakthrough results in these fields can only be achieved by tight communication and knowledge exchange between the two sides. The ultimate goal of this conference is to bring together bioinformaticians and biologists to share experiences in the development and practical application of bioinformatics algorithms.

Topics include:

  • Sequencing technologies
  • Molecular sequence analysis
  • Computational genomics
  • Genome assembly
  • Transcriptomics
  • Metagenomics
  • Immunogenomics

Extended versions of abstracts presented at BiATA 2017 are sought, but this call for papers is fully open to all who want to contribute by submitting a relevant research manuscript.

Dr. Max Alekseyev
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 papers will be 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 850 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

  • Sequencing technologies
  • Molecular sequence analysis
  • Computational genomics
  • Genome assembly
  • Transcriptomics
  • Metagenomics
  • Immunogenomics

Published Papers (3 papers)

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Research

Open AccessArticle Analytic Combinatorics for Computing Seeding Probabilities
Algorithms 2018, 11(1), 3; https://doi.org/10.3390/a11010003
Received: 12 November 2017 / Revised: 7 January 2018 / Accepted: 8 January 2018 / Published: 10 January 2018
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Abstract
Seeding heuristics are the most widely used strategies to speed up sequence alignment in bioinformatics. Such strategies are most successful if they are calibrated, so that the speed-versus-accuracy trade-off can be properly tuned. In the widely used case of read mapping, it has
[...] Read more.
Seeding heuristics are the most widely used strategies to speed up sequence alignment in bioinformatics. Such strategies are most successful if they are calibrated, so that the speed-versus-accuracy trade-off can be properly tuned. In the widely used case of read mapping, it has been so far impossible to predict the success rate of competing seeding strategies for lack of a theoretical framework. Here, we present an approach to estimate such quantities based on the theory of analytic combinatorics. The strategy is to specify a combinatorial construction of reads where the seeding heuristic fails, translate this specification into a generating function using formal rules, and finally extract the probabilities of interest from the singularities of the generating function. The generating function can also be used to set up a simple recurrence to compute the probabilities with greater precision. We use this approach to construct simple estimators of the success rate of the seeding heuristic under different types of sequencing errors, and we show that the estimates are accurate in practical situations. More generally, this work shows novel strategies based on analytic combinatorics to compute probabilities of interest in bioinformatics. Full article
(This article belongs to the Special Issue Bioinformatics Algorithms and Applications)
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Open AccessArticle A Hierarchical Multi-Label Classification Algorithm for Gene Function Prediction
Algorithms 2017, 10(4), 138; https://doi.org/10.3390/a10040138
Received: 28 September 2017 / Revised: 20 October 2017 / Accepted: 28 November 2017 / Published: 8 December 2017
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Abstract
Gene function prediction is a complicated and challenging hierarchical multi-label classification (HMC) task, in which genes may have many functions at the same time and these functions are organized in a hierarchy. This paper proposed a novel HMC algorithm for solving this problem
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Gene function prediction is a complicated and challenging hierarchical multi-label classification (HMC) task, in which genes may have many functions at the same time and these functions are organized in a hierarchy. This paper proposed a novel HMC algorithm for solving this problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph (DAG) and is more difficult to tackle. In the proposed algorithm, the HMC task is firstly changed into a set of binary classification tasks. Then, two measures are implemented in the algorithm to enhance the HMC performance by considering the hierarchy structure during the learning procedures. Firstly, negative instances selecting policy associated with the SMOTE approach are proposed to alleviate the imbalanced data set problem. Secondly, a nodes interaction method is introduced to combine the results of binary classifiers. It can guarantee that the predictions are consistent with the hierarchy constraint. The experiments on eight benchmark yeast data sets annotated by the Gene Ontology show the promising performance of the proposed algorithm compared with other state-of-the-art algorithms. Full article
(This article belongs to the Special Issue Bioinformatics Algorithms and Applications)
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Open AccessArticle Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network
Algorithms 2017, 10(4), 136; https://doi.org/10.3390/a10040136
Received: 17 September 2017 / Revised: 1 December 2017 / Accepted: 2 December 2017 / Published: 5 December 2017
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Abstract
The detection of composite miRNA functional module (CMFM) is of tremendous significance and helps in understanding the organization, regulation and execution of cell processes in cancer, but how to identify functional CMFMs is still a computational challenge. In this paper we propose a
[...] Read more.
The detection of composite miRNA functional module (CMFM) is of tremendous significance and helps in understanding the organization, regulation and execution of cell processes in cancer, but how to identify functional CMFMs is still a computational challenge. In this paper we propose a novel module detection method called MBCFM (detecting Composite Function Modules based on Maximal Biclique enumeration), specifically designed to bicluster miRNAs and target messenger RNAs (mRNAs) on the basis of multiple biological interaction information and topical network features. In this method, we employ algorithm MICA to enumerate all maximal bicliques and further extract R-pairs from the miRNA-mRNA regulatory network. Compared with two existing methods, Mirsynergy and SNMNMF on ovarian cancer dataset, the proposed method of MBCFM is not only able to extract cohesiveness-preserved CMFMs but also has high efficiency in running time. More importantly, MBCFM can be applied to detect other cancer-associated miRNA functional modules. Full article
(This article belongs to the Special Issue Bioinformatics Algorithms and Applications)
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