Special Issue "Application of Informatics and Computing Techniques to Biological Data Processing and Analysis"

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editors

Prof. Dr. Florentino Fdez-Riverola
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Guest Editor
Computer Science Department, Universidad de Vigo, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain
Interests: artificial intelligence; big data; bioinformatics; data mining; machine learning; medical informatics; text mining
Dr. Miguel Rocha
E-Mail Website
Guest Editor
Centre of Biological Engineering/ Department of Informatics, University of Minho, 4710-057 Braga, Portugal
Interests: machine and deep learning; evolutionary computation; bioinformatics; systems biology; constraint-based modeling; metabolomics
Special Issues and Collections in MDPI journals
Dr. Roberto Casado Vara
E-Mail Website
Guest Editor
BISITE research group, University of Salamanca, Edificio Multiusos I+D+i, C/ Espejo s/n, 37007 Salamanca, Spain
Interests: artificial intelligence; nonlinear control; stochastic systems; optimization; blockchain
Special Issues and Collections in MDPI journals
Prof. Dr. Mohd Saberi Mohamad
E-Mail Website
Guest Editor
Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University (UAEU), POBox 17666 AlAin, United Arab Emirates
Interests: artificial intelligence; data science; bioinformatics; computational biology
Special Issues and Collections in MDPI journals

Special Issue Information

Advances in the life sciences today are generating an enormous amount of data that can no longer be analyzed using mere statistical techniques or manually. The field of bioinformatics, a confluence of data analysis techniques and experimental protocols in biomedical sciences, has emerged to solve this problem. The application of computational techniques to biology has enabled enormous advances in genetics thanks to next-generation sequencing or in pharmacology thanks to the in silico design of drugs and active products.

The current use of bioinformatics techniques extends from the mathematical modeling of biological phenomena to the analysis of big data in healthcare to develop community health applications, leading to beneficial improvements in hardware, such as parallelization capacity or computing on graphics cards.

This Special Issue invites original papers that push the boundaries of innovation in bioinformatics and the application of computer science in the life sciences, including those on the applicability of artificial intelligence to these fields, new hardware developments, data analysis pipelines, or use cases in clinical practice.

The particular topics of interest include but are not limited to:

  • Next-generation sequencing;
  • Molecular evolution;
  • High-throughput data analysis;
  • Identification of metabolic pathways;
  • Biomarker identification;
  • Molecular and cellular interactions;
  • In silico optimization of biological systems;
  • Healthcare applications;
  • Biological signal analysis;
  • Artificial Intelligence applied to biological problems;
  • Nonlinear dynamical analysis methods and intelligent signal processing;
  • Feature selection.

Prof. Dr. Florentino Fdez-Riverola
Dr. Miguel Rocha
Dr. Roberto Casado Vara
Prof. Dr. Mohd Saberi Mohamad
Guest Editors

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. Computation 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 1400 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

  • Next generation sequencing
  • Molecular evolution
  • High-throughput data analysis
  • Identification of metabolic pathways
  • Biomarker identification
  • Molecular and cellular interactions
  • In silico optimization of biological systems
  • Health-care applications
  • Biological signal analysis
  • Artificial Intelligence applied to biological problems
  • Non-linear dynamical analysis methods and Intelligent signal processing
  • Feature selection

Published Papers (1 paper)

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Research

Article
Gene Expression Analysis through Parallel Non-Negative Matrix Factorization
Computation 2021, 9(10), 106; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9100106 - 30 Sep 2021
Viewed by 255
Abstract
Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed. It is overwhelming the amount of biological data whose high-dimensional [...] Read more.
Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed. It is overwhelming the amount of biological data whose high-dimensional structure exceeds mostly current computational architectures. The computational time and memory consumption optimization actually become decisive factors in choosing clustering algorithms. We propose a clustering algorithm based on Non-negative Matrix Factorization and K-means to reduce data dimensionality but whilst preserving the biological context and prioritizing gene selection, and it is implemented within parallel GPU-based environments through the CUDA library. A well-known dataset is used in our tests and the quality of the results is measured through the Rand and Accuracy Index. The results show an increase in the acceleration of 6.22× compared to the sequential version. The algorithm is competitive in the biological datasets analysis and it is invariant with respect to the classes number and the size of the gene expression matrix. Full article
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