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Special Issue Dedicated to the 15th International Special Track on Biomedical and Bioinformatics Challenges for Computer Science

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

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 9485

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Special Issue Editors

Dr. Riccardo Dondi

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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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Dr. Mauro Castelli

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NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal
Interests: machine learning; evolutionary computation; deep learning; pattern recognition; neuro-evolution
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Dr. Stefano Beretta

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San Raffaele Telethon Institute for Gene Therapy, Ospedale San Raffaele, 20132 Milan, Italy
Interests: algorithms; bioinformatics; computational biology; graph theory; biology

Prof. Dr. Rodrigo Weber Dos Santos

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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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Prof. Dr. Mario Cannataro

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Department of Medical and Surgical Sciences, University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy
Interests: bioinformatics, parallel computing, artificial intelligence, data mining
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Dr. Giuseppe Agapito

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Department of Legal, Historical, Economic and SocialSciences, University "Magna Græcia" of Catanzaro, 88100 Catanzaro, Italy
Interests: distributed computing; bioinformatics; machine learning
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Dr. Italo Zoppis

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Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy
Interests: machine learning; artificial intelligence
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Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to the 15th International Special Track on Biomedical and Bioinformatics Challenges for Computer Science, held in conjunction with ICCS 2022 (International Conference on Computational Science 2022). The track deals with novel computational concepts, methods, and tools that can be used to tackle the growing complexity in the analysis of data generated by emerging technologies in biomedicine and bioinformatics.

The aim of this track is to bring together computer science and life scientists to discuss emerging and future directions in topics related to key bioinformatics and computational biology techniques:

Advanced computing architectures for bioinformatics and biomedicine;
Algorithms for bioinformatics;
Data analysis and knowledge discovery for bioinformatics and biomedicine;
Data management and integration in bioinformatics and biomedicine;
The integration of quantitative/symbolic knowledge into executable biomedical “theories” or models.

The authors of selected papers from the track will be invited to submit extended versions of their original contributions. Moreover, papers related to the track themes are also welcome.

Dr. Stefano Beretta
Prof. Dr. Rodrigo Weber Dos Santos
Dr. Riccardo Dondi
Prof. Dr. Mario Cannataro
Dr. Mauro Castelli
Dr. Giuseppe Agapito
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. 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.

Keywords

Algorithms for bioinformatics
Computational and mathematical models for biomedical and biological systems and phenomena
Data analysis for bioinformatics and biomedicine
Data integration in bioinformatics and biomedicine
Data mining in bioinformatics and biomedicine
Data management in bioinformatics and biomedicine
High performance computing
Knowledge representation in bioinformatics and biomedicine
Machine learning applications in bioinformatics and biomedicine

Published Papers (5 papers)

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Research

20 pages, 34080 KiB

Open AccessArticle

A Hybrid Model for Cardiac Perfusion: Coupling a Discrete Coronary Arterial Tree Model with a Continuous Porous-Media Flow Model of the Myocardium

by João R. Alves, Lucas A. Berg, Evandro D. Gaio, Bernardo M. Rocha, Rafael A. B. de Queiroz and Rodrigo W. dos Santos

Entropy 2023, 25(8), 1229; https://doi.org/10.3390/e25081229 - 18 Aug 2023

Viewed by 925

Abstract

This paper presents a novel hybrid approach for the computational modeling of cardiac perfusion, combining a discrete model of the coronary arterial tree with a continuous porous-media flow model of the myocardium. The constructive constrained optimization (CCO) algorithm captures the detailed topology and geometry of the coronary arterial tree network, while Poiseuille’s law governs blood flow within this network. Contrast agent dynamics, crucial for cardiac MRI perfusion assessment, are modeled using reaction–advection–diffusion equations within the porous-media framework. The model incorporates fibrosis–contrast agent interactions and considers contrast agent recirculation to simulate myocardial infarction and Gadolinium-based late-enhancement MRI findings. Numerical experiments simulate various scenarios, including normal perfusion, endocardial ischemia resulting from stenosis, and myocardial infarction. The results demonstrate the model’s efficacy in establishing the relationship between blood flow and stenosis in the coronary arterial tree and contrast agent dynamics and perfusion in the myocardial tissue. The hybrid model enables the integration of information from two different exams: computational fractional flow reserve (cFFR) measurements of the heart coronaries obtained from CT scans and heart perfusion and anatomy derived from MRI scans. The cFFR data can be integrated with the discrete arterial tree, while cardiac perfusion MRI data can be incorporated into the continuum part of the model. This integration enhances clinical understanding and treatment strategies for managing cardiovascular disease. Full article

(This article belongs to the Special Issue Special Issue Dedicated to the 15th International Special Track on Biomedical and Bioinformatics Challenges for Computer Science)

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21 pages, 1573 KiB

Open AccessArticle

A 3D Approach Using a Control Algorithm to Minimize the Effects on the Healthy Tissue in the Hyperthermia for Cancer Treatment

by Gustavo Resende Fatigate, Marcelo Lobosco and Ruy Freitas Reis

Entropy 2023, 25(4), 684; https://doi.org/10.3390/e25040684 - 19 Apr 2023

Cited by 1 | Viewed by 1471

Abstract

According to the World Health Organization, cancer is a worldwide health problem. Its high mortality rate motivates scientists to study new treatments. One of these new treatments is hyperthermia using magnetic nanoparticles. This treatment consists in submitting the target region with a low-frequency magnetic field to increase its temperature over 43 °C, as the threshold for tissue damage and leading the cells to necrosis. This paper uses an in silico three-dimensional Pennes’ model described by a set of partial differential equations (PDEs) to estimate the percentage of tissue damage due to hyperthermia. Differential evolution, an optimization method, suggests the best locations to inject the nanoparticles to maximize tumor cell death and minimize damage to healthy tissue. Three different scenarios were performed to evaluate the suggestions obtained by the optimization method. The results indicate the positive impact of the proposed technique: a reduction in the percentage of healthy tissue damage and the complete damage of the tumors were observed. In the best scenario, the optimization method was responsible for decreasing the healthy tissue damage by

59 %

when the nanoparticles injection sites were located in the non-intuitive points indicated by the optimization method. The numerical solution of the PDEs is computationally expensive. This work also describes the implemented parallel strategy based on CUDA to reduce the computational costs involved in the PDEs resolution. Compared to the sequential version executed on the CPU, the proposed parallel implementation was able to speed the execution time up to

84.4

times. Full article

(This article belongs to the Special Issue Special Issue Dedicated to the 15th International Special Track on Biomedical and Bioinformatics Challenges for Computer Science)

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18 pages, 794 KiB

Open AccessArticle

Design and Implementation of a New Local Alignment Algorithm for Multilayer Networks

by Marianna Milano, Pietro Hiram Guzzi and Mario Cannataro

Entropy 2022, 24(9), 1272; https://doi.org/10.3390/e24091272 - 09 Sep 2022

Cited by 4 | Viewed by 1170

Abstract

Network alignment (NA) is a popular research field that aims to develop algorithms for comparing networks. Applications of network alignment span many fields, from biology to social network analysis. NA comes in two forms: global network alignment (GNA), which aims to find a global similarity, and LNA, which aims to find local regions of similarity. Recently, there has been an increasing interest in introducing complex network models such as multilayer networks. Multilayer networks are common in many application scenarios, such as modelling of relations among people in a social network or representing the interplay of different molecules in a cell or different cells in the brain. Consequently, the need to introduce algorithms for the comparison of such multilayer networks, i.e., local network alignment, arises. Existing algorithms for LNA do not perform well on multilayer networks since they cannot consider inter-layer edges. Thus, we propose local alignment of multilayer networks (MultiLoAl), a novel algorithm for the local alignment of multilayer networks. We define the local alignment of multilayer networks and propose a heuristic for solving it. We present an extensive assessment indicating the strength of the algorithm. Furthermore, we implemented a synthetic multilayer network generator to build the data for the algorithm’s evaluation. Full article

(This article belongs to the Special Issue Special Issue Dedicated to the 15th International Special Track on Biomedical and Bioinformatics Challenges for Computer Science)

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17 pages, 590 KiB

Open AccessFeature PaperArticle

Forecasting COVID-19 Epidemic Trends by Combining a Neural Network with R_t Estimation

by Pietro Cinaglia and Mario Cannataro

Entropy 2022, 24(7), 929; https://doi.org/10.3390/e24070929 - 04 Jul 2022

Cited by 13 | Viewed by 2589

Abstract

On 31 December 2019, a cluster of pneumonia cases of unknown etiology was reported in Wuhan (China). The cases were declared to be Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO). COVID-19 has been defined as SARS Coronavirus 2 (SARS-CoV-2). Some countries, e.g., Italy, France, and the United Kingdom (UK), have been subjected to frequent restrictions for preventing the spread of infection, contrary to other ones, e.g., the United States of America (USA) and Sweden. The restrictions afflicted the evolution of trends with several perturbations that destabilized its normal evolution. Globally,

R_{t}

has been used to estimate time-varying reproduction numbers during epidemics. Methods: This paper presents a solution based on Deep Learning (DL) for the analysis and forecasting of epidemic trends in new positive cases of SARS-CoV-2 (COVID-19). It combined a neural network (NN) and an

R_{t}

estimation by adjusting the data produced by the output layer of the NN on the related

R_{t}

estimation. Results: Tests were performed on datasets related to the following countries: Italy, the USA, France, the UK, and Sweden. Positive case registration was retrieved between 24 February 2020 and 11 January 2022. Tests performed on the Italian dataset showed that our solution reduced the Mean Absolute Percentage Error (MAPE) by 28.44%, 39.36%, 22.96%, 17.93%, 28.10%, and 24.50% compared to other ones with the same configuration but that were based on the LSTM, GRU, RNN, ARIMA (1,0,3), and ARIMA (7,2,4) models, or an NN without applying the

R_{t}

as a corrective index. It also reduced MAPE by 17.93%, the Mean Absolute Error (MAE) by 34.37%, and the Root Mean Square Error (RMSE) by 43.76% compared to the same model without the adjustment performed by the

R_{t}

. Furthermore, it allowed an average MAPE reduction of 5.37%, 63.10%, 17.84%, and 14.91% on the datasets related to the USA, France, the UK, and Sweden, respectively. Full article

(This article belongs to the Special Issue Special Issue Dedicated to the 15th International Special Track on Biomedical and Bioinformatics Challenges for Computer Science)

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16 pages, 487 KiB

Open AccessFeature PaperArticle

An Extensive Assessment of Network Embedding in PPI Network Alignment

by Marianna Milano, Chiara Zucco, Marzia Settino and Mario Cannataro

Entropy 2022, 24(5), 730; https://doi.org/10.3390/e24050730 - 20 May 2022

Cited by 2 | Viewed by 2077

Abstract

Network alignment is a fundamental task in network analysis. In the biological field, where the protein–protein interaction (PPI) is represented as a graph, network alignment allowed the discovery of underlying biological knowledge such as conserved evolutionary pathways and functionally conserved proteins throughout different species. A recent trend in network science concerns network embedding, i.e., the modelling of nodes in a network as a low-dimensional feature vector. In this survey, we present an overview of current PPI network embedding alignment methods, a comparison among them, and a comparison to classical PPI network alignment algorithms. The results of this comparison highlight that: (i) only five network embeddings for network alignment algorithms have been applied in the biological context, whereas the literature presents several classical network alignment algorithms; (ii) there is a need for developing an evaluation framework that may enable a unified comparison between different algorithms; (iii) the majority of the proposed algorithms perform network embedding through matrix factorization-based techniques; (iv) three out of five algorithms leverage external biological resources, while the remaining two are designed for domain agnostic network alignment and tested on PPI networks; (v) two algorithms out of three are stated to perform multi-network alignment, while the remaining perform pairwise network alignment. Full article

(This article belongs to the Special Issue Special Issue Dedicated to the 15th International Special Track on Biomedical and Bioinformatics Challenges for Computer Science)

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Special Issue Dedicated to the 15th International Special Track on Biomedical and Bioinformatics Challenges for Computer Science

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