Biomedical and Bioinformatics Challenges for Computer Science

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (25 September 2018) | Viewed by 17237

Special Issue Editors


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NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1070-312 Lisboa, Portugal
Interests: genetic programming; evolutionary computation; machine learning; neuroevolution; pattern recognition
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Department of Literature, Philosophy, Communication Studies, University of Bergamo, 24129 Bergamo, Italy
Interests: algorithm design; computational and parameterized complexity for problems in computational biology; bioinformatics and network analysis
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Department of Computer Science, Universidade Federal de Juiz de Fora, Juiz de Fora 36036-330, Brazil
Interests: computational modeling; biomedical engineering; numerical methods; mathematical biology; high-performance computing
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Guest Editor
Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
Interests: bioinformatics; computational biology; parallel computing; algorithms; distributed computing; computational simulation; applied bioinformatics; artificial and computational intelligence

Special Issue Information

Dear Colleagues,

Emerging technologies in genomics, transcriptomics, metagenomics, and other life science areas are generating an increasing amount of complex data and information. These recent changes related to emerging technologies have made the role of computer science (both in theoretical and applied aspects) much more critical in all the bioinformatics research directions. In order to tackle the growing complexity associated with the management of huge amount of data, researchers need to explore, develop, and apply novel computational concepts, methods, tools, and systems. Many of these new approaches are likely to involve advanced and large-scale computing techniques, computational approaches, technologies and infrastructures.

This Special Issue invites submissions on topics related to these research directions in bioinformatics, such as high-performance architectures and systems, distributed computing (e.g., grid, cloud, peer-to-peer, Web services, e-infrastructures), computational simulation (mechanistic, stochastic, multi-model), algorithms (theoretical and experimental aspects) design and analysis, applied bioinformatics (analysis pipelines, software tools, preprocessing, analysis and integration of clinical and omics data), and artificial and computational intelligence (machine learning, agents, evolutionary techniques, and bio-inspired methods).

The Special Issue also specifically invites extended versions of papers presented at the “Workshop on Biomedical and Bioinformatics Challenges for Computer Science”. Submitted papers should be extended to the size of regular research or review articles with 50% extension of new results. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in Open Access format in Computers and collected together in this Special Issue website.

Dr. Giuseppe Agapito
Prof. Mario Cannataro
Dr. Mauro Castelli
Dr. Riccardo Dondi
Prof. Rodrigo Weber dos Santos
Dr. Italo Zoppis
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 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. Computers 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 1800 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

  • High-performance architectures and systems (e.g. multicore, GPU);
  • Distributed computing (e.g. grid, cloud, peer-to-peer, Web services, e-infrastructures);
  • Computational simulation (mechanistic, stochastic, multi-model);
  • Algorithms (theoretical and experimental aspects);
  • Applied bioinformatics (analysis pipelines, tools, applications);
  • Artificial and computational intelligence (machine learning, agents, evolutionary techniques, bio-inspired methods).

Published Papers (3 papers)

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Editorial

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2 pages, 152 KiB  
Editorial
Editorial of the Special Issue of the 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science—BBC 2017
by Giuseppe Agapito, Mario Cannataro, Mauro Castelli, Riccardo Dondi and Italo Zoppis
Computers 2018, 7(1), 17; https://doi.org/10.3390/computers7010017 - 26 Feb 2018
Cited by 1 | Viewed by 4868
Abstract
In this special issue, we present two of the papers presented at the 10th Workshop on Biomedical and Bioinformatics Challenges for Computer Science—BBC2017, held in Zurich, 12–14 June 2017. Full article
(This article belongs to the Special Issue Biomedical and Bioinformatics Challenges for Computer Science)

Research

Jump to: Editorial

1338 KiB  
Article
On the Use of Voice Signals for Studying Sclerosis Disease
by Patrizia Vizza, Giuseppe Tradigo, Domenico Mirarchi, Roberto Bruno Bossio and Pierangelo Veltri
Computers 2017, 6(4), 30; https://doi.org/10.3390/computers6040030 - 28 Nov 2017
Cited by 2 | Viewed by 6523
Abstract
Multiple sclerosis (MS) is a chronic demyelinating autoimmune disease affecting the central nervous system. One of its manifestations concerns impaired speech, also known as dysarthria. In many cases, a proper speech evaluation can play an important role in the diagnosis of MS. The [...] Read more.
Multiple sclerosis (MS) is a chronic demyelinating autoimmune disease affecting the central nervous system. One of its manifestations concerns impaired speech, also known as dysarthria. In many cases, a proper speech evaluation can play an important role in the diagnosis of MS. The identification of abnormal voice patterns can provide valid support for a physician in the diagnosing and monitoring of this neurological disease. In this paper, we present a method for vocal signal analysis in patients affected by MS. The goal is to identify the dysarthria in MS patients to perform an early diagnosis of the disease and to monitor its progress. The proposed method provides the acquisition and analysis of vocal signals, aiming to perform feature extraction and to identify relevant patterns useful to impaired speech associated with MS. This method integrates two well-known methodologies, acoustic analysis and vowel metric methodology, to better define pathological compared to healthy voices. As a result, this method provides patterns that could be useful indicators for physicians in identifying patients affected by MS. Moreover, the proposed procedure could be a valid support in early diagnosis as well as in monitoring treatment success, thus improving a patient’s life quality. Full article
(This article belongs to the Special Issue Biomedical and Bioinformatics Challenges for Computer Science)
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485 KiB  
Article
Application of Machine Learning Models in Error and Variant Detection in High-Variation Genomics Datasets
by Milko Krachunov, Maria Nisheva and Dimitar Vassilev
Computers 2017, 6(4), 29; https://doi.org/10.3390/computers6040029 - 10 Nov 2017
Cited by 2 | Viewed by 5035
Abstract
For metagenomics datasets, datasets of complex polyploid genomes, and other high-variation genomics datasets, there are difficulties with the analysis, error detection and variant calling, stemming from the challenges of discerning sequencing errors from biological variation. Confirming base candidates with high frequency of occurrence [...] Read more.
For metagenomics datasets, datasets of complex polyploid genomes, and other high-variation genomics datasets, there are difficulties with the analysis, error detection and variant calling, stemming from the challenges of discerning sequencing errors from biological variation. Confirming base candidates with high frequency of occurrence is no longer a reliable measure because of the natural variation and the presence of rare bases. The paper discusses an approach to the application of machine learning models to classify bases into erroneous and rare variations after preselecting potential error candidates with a weighted frequency measure, which aims to focus on unexpected variations by using the inter-sequence pairwise similarity. Different similarity measures are used to account for different types of datasets. Four machine learning models are implemented and tested. Full article
(This article belongs to the Special Issue Biomedical and Bioinformatics Challenges for Computer Science)
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