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Mathematics
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Algorithms and Models for Bioinformatics and Biomedical Applications
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Algorithms and Models for Bioinformatics and Biomedical Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 6822

Special Issue Editors

Prof. Dr. Filippo Geraci

E-Mail Website
Guest Editor
Institute for Informatics and Telematics, CNR, 56124 Pisa, Italy
Interests: information engineering; bioinformaics
Prof. Dr. Marco Fornili

E-Mail Website
Guest Editor
Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy
Interests: bioinformaics

Special Issue Information

Dear Colleagues,

Omics data analysis has fulfilled its promise of shedding light on many complex mechanisms that regulate life, including the onset and progression of diseases. In addition to being intrinsically difficult to tackle, distilling knowledge from these data is complicated, requiring a joint effort between the life science and computer science communities. Efficiency in dealing with the enormous size of genomic data is not sufficient for algorithms to derive meaningful information unless the underlying computational model is coherent with biology. On the other hand, the complexity of the relationships among the elements that determine cell phenotypes cannot be captured if not computationally. Mathematical models are ubiquitous in bioinformatic and biomedical applications and have contributed to the solution of practical problems. Notable examples include the distribution of expression levels to infer dysregulated genes, the assessment of biomarkers, and the identification of families of cooperating proteins. The aim of this Special Issue is to collect contributions related to new algorithms and models that can solve practical problems in bioinformatics and biomedicine.

Topics of such contributions may include, but are not limited to:

  • Machine learning and data mining applied to biomedical data.
  • Computational and mathematical models for identification of biomarkers.
  • Methods of visualization of high-dimensional omic data.
  • Pattern recognition for biomedical data.
  • Integration of clinical, heath and omic data.
  • Text mining in bioinformatics and biomedicine.
  • Personalized diagnosis and prognosis.
  • Simulation and modeling of biological systems.
  • Classification and clustering algorithms for bioinformatics problems.
  • Genetic algorithms.
  • Biological sequence alignment or analysis.

Prof. Dr. Filippo Geraci
Prof. Dr. Marco Fornili
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. Mathematics is an international peer-reviewed open access semimonthly 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.

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Published Papers (5 papers)

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22 pages, 331 KiB  
Article
On an Impulsive Conformable M1 Oncolytic Virotherapy Neural Network Model: Stability of Sets Analysis
by Gani Stamov, Ivanka Stamova and Cvetelina Spirova
Mathematics 2025, 13(1), 141; https://doi.org/10.3390/math13010141 - 2 Jan 2025
Viewed by 436
Abstract
In this paper, the impulsive conformable calculus approach is applied to the introduction of an M1 oncolytic virotherapy neural network model. The proposed model extends some existing mathematical models that describe the dynamics of the concentrations of normal cells, tumor cells, nutrients, [...] Read more.
In this paper, the impulsive conformable calculus approach is applied to the introduction of an M1 oncolytic virotherapy neural network model. The proposed model extends some existing mathematical models that describe the dynamics of the concentrations of normal cells, tumor cells, nutrients, M1 viruses and cytotoxic T lymphocyte (CTL) cells to the impulsive conformable setting. The conformable concept allows for flexibility in the modeling approach, as well as avoiding the complexity of using classical fractional derivatives. The impulsive generalization supports the application of a suitable impulsive control therapy. Reaction–diffusion terms are also considered. We analyze the stable behavior of sets of states, which extend the investigations of the dynamics of separate equilibrium points. By applying the impulsive conformable Lyapunov function technique, sufficient conditions for the uniform global exponential stability of sets of states are established. An example is also presented to illustrate our results. Full article
(This article belongs to the Special Issue Algorithms and Models for Bioinformatics and Biomedical Applications)
26 pages, 1717 KiB  
Article
Feature Selection and Machine Learning Approaches for Detecting Sarcopenia Through Predictive Modeling
by Akhrorbek Tukhtaev, Dilmurod Turimov, Jiyoun Kim and Wooseong Kim
Mathematics 2025, 13(1), 98; https://doi.org/10.3390/math13010098 - 29 Dec 2024
Viewed by 616
Abstract
Sarcopenia is an age-associated condition characterized by a muscle mass and function decline. This condition poses significant health risks for the elderly. This study developed a machine-learning model to predict sarcopenia using data from 664 participants. Key features were identified using the Local [...] Read more.
Sarcopenia is an age-associated condition characterized by a muscle mass and function decline. This condition poses significant health risks for the elderly. This study developed a machine-learning model to predict sarcopenia using data from 664 participants. Key features were identified using the Local Interpretable Model-Agnostic Explanations (LIME) method. This enhanced model interpretability. Additionally, the CatBoost algorithm was used for training, and SMOTE-Tomek addressed dataset imbalance. Notably, the reduced-feature model outperformed the full-feature model, achieving an accuracy of 0.89 and an AUC of 0.94. The results highlight the importance of feature selection for improving model efficiency and interpretability in clinical applications. This approach provides valuable insights into the early identification and management of sarcopenia, contributing to better patient outcomes. Full article
(This article belongs to the Special Issue Algorithms and Models for Bioinformatics and Biomedical Applications)
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26 pages, 12239 KiB  
Article
Deep Learning-Based Intelligent Diagnosis of Lumbar Diseases with Multi-Angle View of Intervertebral Disc
by Kaisi (Kathy) Chen, Lei Zheng, Honghao Zhao and Zihang Wang
Mathematics 2024, 12(13), 2062; https://doi.org/10.3390/math12132062 - 1 Jul 2024
Viewed by 1317
Abstract
The diagnosis of degenerative lumbar spine disease mainly relies on clinical manifestations and imaging examinations. However, the clinical manifestations are sometimes not obvious, and the diagnosis of medical imaging is usually time-consuming and highly relies on the doctor’s personal experiences. Therefore, a smart [...] Read more.
The diagnosis of degenerative lumbar spine disease mainly relies on clinical manifestations and imaging examinations. However, the clinical manifestations are sometimes not obvious, and the diagnosis of medical imaging is usually time-consuming and highly relies on the doctor’s personal experiences. Therefore, a smart diagnostic technology that can assist doctors in manual diagnosis has become particularly urgent. Taking advantage of the development of artificial intelligence, a series of solutions have been proposed for the diagnosis of spinal diseases by using deep learning methods. The proposed methods produce appealing results, but the majority of these approaches are based on sagittal and axial images separately, which limits the capability of different deep learning methods due to the insufficient use of data. In this article, we propose a two-stage classification process that fully utilizes image data. In particular, in the first stage, we used the Mask RCNN model to identify the lumbar spine in the spine image, locate the position of the vertebra and disc, and complete rough classification. In the fine classification stage, a multi-angle view of the intervertebral disc is generated by splicing the sagittal and axial slices of the intervertebral disc up and down based on the key position identified in the first stage, which provides more pieces of information to the deep learning methods for classification. The experimental results reveal substantial performance enhancements with the synthesized multi-angle view, achieving an F1 score of 96.67%. This represents a performance increase of approximately 15% over the sagittal images at 84.48% and nearly 14% over the axial images at 83.15%. This indicates that the proposed paradigm is feasible and more effective in identifying spinal-related degenerative diseases through medical images. Full article
(This article belongs to the Special Issue Algorithms and Models for Bioinformatics and Biomedical Applications)
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18 pages, 1645 KiB  
Article
A Study on Graph Centrality Measures of Different Diseases Due to DNA Sequencing
by Ghulam Muhiuddin, Sovan Samanta, Abdulrahman F. Aljohani and Abeer M. Alkhaibari
Mathematics 2023, 11(14), 3166; https://doi.org/10.3390/math11143166 - 19 Jul 2023
Cited by 2 | Viewed by 1872
Abstract
Rare genetic diseases are often caused by single-gene defects that affect various biological processes across different scales. However, it is challenging to identify the causal genes and understand the molecular mechanisms of these diseases. In this paper, we present a multiplex network approach [...] Read more.
Rare genetic diseases are often caused by single-gene defects that affect various biological processes across different scales. However, it is challenging to identify the causal genes and understand the molecular mechanisms of these diseases. In this paper, we present a multiplex network approach to study the relationship between human diseases and genes. We construct a human disease network (HDN) and a human genome network (HGN) based on genotype–phenotype associations and gene interactions, respectively. We analyze 3771 rare diseases and find distinct phenotypic modules within each dimension that reflect the functional effects of gene mutations. These modules can also be used to predict novel gene candidates for unsolved rare diseases and to explore the cross-scale impact of gene perturbations. We compute various centrality measures for both networks and compare them. Our main finding is that diseases are weakly connected in the HDN, while genes are strongly connected in the HGN. This implies that diseases are relatively isolated from each other, while genes are involved in multiple biological processes. This result has implications for understanding the transmission of infectious diseases and the development of therapeutic interventions. We also show that not all diseases have the same potential to spread infections to other parts of the body, depending on their centrality in the HDN. Our results show that the phenotypic module formalism can capture the complexity of rare diseases beyond simple physical interaction networks and can be applied to study diseases arising from DNA (Deoxyribonucleic Acid) sequencing errors. This study provides a novel network-based framework for integrating multi-scale data and advancing the understanding and diagnosis of rare genetic diseases. Full article
(This article belongs to the Special Issue Algorithms and Models for Bioinformatics and Biomedical Applications)
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20 pages, 599 KiB  
Article
seq2R: An R Package to Detect Change Points in DNA Sequences
by Nora M. Villanueva, Marta Sestelo, Miguel M. Fonseca and Javier Roca-Pardiñas
Mathematics 2023, 11(10), 2299; https://doi.org/10.3390/math11102299 - 15 May 2023
Viewed by 1562
Abstract
Identifying the mutational processes that shape the nucleotide composition of the mitochondrial genome (mtDNA) is fundamental to better understand how these genomes evolve. Several methods have been proposed to analyze DNA sequence nucleotide composition and skewness, but most of them lack any measurement [...] Read more.
Identifying the mutational processes that shape the nucleotide composition of the mitochondrial genome (mtDNA) is fundamental to better understand how these genomes evolve. Several methods have been proposed to analyze DNA sequence nucleotide composition and skewness, but most of them lack any measurement of statistical support or were not developed taking into account the specificities of mitochondrial genomes. A new methodology is presented, which is specifically developed for mtDNA to detect compositional changes or asymmetries (AT and CG skews) based on nonparametric regression models and their derivatives. The proposed method also includes the construction of confidence intervals, which are built using bootstrap techniques. This paper introduces an R package, known as seq2R, that implements the proposed methodology. Moreover, an illustration of the use of seq2R is provided using real data, specifically two publicly available complete mtDNAs: the human (Homo sapiens) sequence and a nematode (Radopholus similis) mitogenome sequence. Full article
(This article belongs to the Special Issue Algorithms and Models for Bioinformatics and Biomedical Applications)
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