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Entropy
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Computational Methods and Algorithms for Bioinformatics
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Computational Methods and Algorithms for Bioinformatics

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

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 14044

Special Issue Editors

Dr. Camelia Chira

E-Mail Website
Guest Editor
Faculty of Mathematics and Computer Science, Babes-Bolyai University, 400084 Cluj-Napoca, Romania
Interests: evolutionary computation; nature-inspired metaheuristics; complex networks; bioinformatics
Dr. Iuliana Bocicor

E-Mail Website
Guest Editor
Faculty of Mathematics and Computer Science, Babes-Bolyai University, 400084 Cluj-Napoca, Romania
Interests: machine learning; bioinformatics; artificial intelligence; data mining
Dr. Crina Grosan

E-Mail Website
Guest Editor
Applied Technologies for Clinical Care, King's College London, London WC2R 2LS, UK
Interests: machine learning; optimization; data analytics; complex systems

Special Issue Information

Dear Colleagues,

Recent technological developments have led to increasingly large amounts of biological data. In this context, the development and application of algorithms and computational methods for the modeling and analysis of biological and health-related data are essential for knowledge discovery, enabling necessary additional insight towards understanding biological processes and systems. In this Special Issue, we welcome contributions that report innovative applications leading to the advancement of biological and medical research with the support of computer science methods and tools.

Areas of interest in the context of this Special Issue include the following:

  • Development, evaluation, and application of novel algorithms and computational models for data analysis and knowledge discovery within biological and health-related data.
  • Adaptation and evaluation of existing algorithms for the analysis and interpretation of data related to molecular or structural biology (e.g., but not restricted to, genomics, proteomics, systems biology).
  • Databases, open-source software, web-services for storing, management, organization, and processing of biological data.

Potential topics of the Special Issue include but are not limited to the following: data mining and optimization methods in bioinformatics; systems biology and biological networks; gene expression analysis; sequence analysis; structure prediction; pattern recognition; clustering and classification; computational intelligence; databases and data management; image analysis; machine learning; algorithms and software tools; web services in bioinformatics; computational evolutionary biology; computational genomics and proteomics; molecular interactions; functional association networks; protein dynamics; drug design; analysis and progression of disease.

Submitted manuscripts are expected to describe original work, including methods, techniques, applications, tools, or survey papers. We encourage the inclusion of comparisons with alternative methods. Authors may also wish to provide a short publicly-accessible video.

Dr. Camelia Chira
Dr. Iuliana Bocicor
Dr. Crina Grosan
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

  • bioinformatics
  • computational biology
  • systems biology
  • data mining
  • pattern recognition
  • image analysis
  • computational intelligence
  • machine learning

Published Papers (5 papers)

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Research

14 pages, 5095 KiB  
Article
massiveGST: A Mann–Whitney–Wilcoxon Gene-Set Test Tool That Gives Meaning to Gene-Set Enrichment Analysis
by Luigi Cerulo and Stefano Maria Pagnotta
Entropy 2022, 24(5), 739; https://doi.org/10.3390/e24050739 - 23 May 2022
Viewed by 2369
Abstract
Gene-set enrichment analysis is the key methodology for obtaining biological information from transcriptomic space’s statistical result. Since its introduction, Gene-set Enrichment analysis methods have obtained more reliable results and a wider range of application. Great attention has been devoted to global tests, in [...] Read more.
Gene-set enrichment analysis is the key methodology for obtaining biological information from transcriptomic space’s statistical result. Since its introduction, Gene-set Enrichment analysis methods have obtained more reliable results and a wider range of application. Great attention has been devoted to global tests, in contrast to competitive methods that have been largely ignored, although they appear more flexible because they are independent from the source of gene-profiles. We analyzed the properties of the Mann–Whitney–Wilcoxon test, a competitive method, and adapted its interpretation in the context of enrichment analysis by introducing a Normalized Enrichment Score that summarize two interpretations: a probability estimate and a location index. Two implementations are presented and compared with relevant literature methods: an R package and an online web tool. Both allow for obtaining tabular and graphical results with attention to reproducible research. Full article
(This article belongs to the Special Issue Computational Methods and Algorithms for Bioinformatics)
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12 pages, 5154 KiB  
Article
Simulation of Cardiac Flow under the Septal Defect Based on Lattice Boltzmann Method
by Zhengdao Wang, Xiandong Zhang, Yumeng Li, Hui Yang, Haihong Xue, Yikun Wei and Yuehong Qian
Entropy 2022, 24(2), 187; https://doi.org/10.3390/e24020187 - 27 Jan 2022
Viewed by 2099
Abstract
In this paper, the lattice Boltzmann method was used to simulate the cardiac flow in children with aseptal defect. The inner wall model of the heart was reconstructed from 210 computed tomography scans. By simulating and comparing the cardiac flow field, the pressure [...] Read more.
In this paper, the lattice Boltzmann method was used to simulate the cardiac flow in children with aseptal defect. The inner wall model of the heart was reconstructed from 210 computed tomography scans. By simulating and comparing the cardiac flow field, the pressure field, the blood oxygen content, and the distribution of entropy generation before and after an operation, the effects of septal defect on pulmonary hypertension(PH), cyanosis, and heart load were analyzed in detail. It is found that the atrial septal defect(ASD) of the child we analyzed had a great influence on the blood oxygen content in the pulmonary artery, which leads to lower efficiency of oxygen binding in the lungs and increases the burden on the heart. At the same time, it also significantly enhanced the entropy generation rate of the cardiac flow, which also leads to a higher heart load. However, the main cause of PH is not ASD, but ventricular septal defect (VSD). Meanwhile, it significantly reduced the blood oxygen content in the brachiocephalic trunk, but rarely affects the blood oxygen contents in the downstream left common carotid artery, left subclavian artery, and descending aorta are not significantly affected by VSD. It causes severe cyanosis on the face and lips. Full article
(This article belongs to the Special Issue Computational Methods and Algorithms for Bioinformatics)
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15 pages, 426 KiB  
Article
AutoPPI: An Ensemble of Deep Autoencoders for Protein–Protein Interaction Prediction
by Gabriela Czibula, Alexandra-Ioana Albu, Maria Iuliana Bocicor and Camelia Chira
Entropy 2021, 23(6), 643; https://doi.org/10.3390/e23060643 - 21 May 2021
Cited by 13 | Viewed by 3114
Abstract
Proteins are essential molecules, that must correctly perform their roles for the good health of living organisms. The majority of proteins operate in complexes and the way they interact has pivotal influence on the proper functioning of such organisms. In this study we [...] Read more.
Proteins are essential molecules, that must correctly perform their roles for the good health of living organisms. The majority of proteins operate in complexes and the way they interact has pivotal influence on the proper functioning of such organisms. In this study we address the problem of protein–protein interaction and we propose and investigate a method based on the use of an ensemble of autoencoders. Our approach, entitled AutoPPI, adopts a strategy based on two autoencoders, one for each type of interactions (positive and negative) and we advance three types of neural network architectures for the autoencoders. Experiments were performed on several data sets comprising proteins from four different species. The results indicate good performances of our proposed model, with accuracy and AUC values of over 0.97 in all cases. The best performing model relies on a Siamese architecture in both the encoder and the decoder, which advantageously captures common features in protein pairs. Comparisons with other machine learning techniques applied for the same problem prove that AutoPPI outperforms most of its contenders, for the considered data sets. Full article
(This article belongs to the Special Issue Computational Methods and Algorithms for Bioinformatics)
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13 pages, 1148 KiB  
Article
A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization
by Delia Dumitru, Laura Dioșan, Anca Andreica and Zoltán Bálint
Entropy 2021, 23(4), 414; https://doi.org/10.3390/e23040414 - 31 Mar 2021
Cited by 8 | Viewed by 2058
Abstract
Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need [...] Read more.
Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need for a ground truth that is not always accessible. Therefore, a specialized detector that is optimized for the image particularities can help improve edge detection performance. In this paper, we apply transfer learning to optimize cellular automata (CA) rules for edge detection using particle swarm optimization (PSO). Cellular automata provide fast computation, while rule optimization provides adaptability to the properties of the target images. We use transfer learning from synthetic to medical images because expert-annotated medical data is typically difficult to obtain. We show that our method is tunable for medical images with different properties, and we show that, for more difficult edge detection tasks, batch optimization can be used to boost the quality of the edges. Our method is suitable for the identification of structures, such as cardiac cavities on medical images, and could be used as a component of an automatic radiology decision support tool. Full article
(This article belongs to the Special Issue Computational Methods and Algorithms for Bioinformatics)
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15 pages, 859 KiB  
Article
Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data
by Claudia Cava, Soudabeh Sabetian and Isabella Castiglioni
Entropy 2021, 23(2), 225; https://doi.org/10.3390/e23020225 - 11 Feb 2021
Cited by 6 | Viewed by 2854
Abstract
The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in [...] Read more.
The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy. Full article
(This article belongs to the Special Issue Computational Methods and Algorithms for Bioinformatics)
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