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Mathematics
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Advanced Computational Biology and Bioinformatics
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Advanced Computational Biology and Bioinformatics

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 7243

Special Issue Editors

Dr. Jordi Martorell-Marugán

E-Mail Website
Guest Editor
1. Data Science for Health Research Unit, Fondazione Bruno Kessler, 38123 Trento, Italy
2. Department of Statistics, Faculty of Medicine, University of Granada, 18071 Granada, Spain
3. Centre for Genomics and Oncological Research, Pfizer-University of Granada-Andalusian Regional Government, 18016 Granada, Spain
Interests: bioinformatics; computational biology; omics data analysis; machine learning
Dr. Pedro Carmona-Sáez

E-Mail Website
Guest Editor
1. Department of Statistics, Faculty of Medicine, University of Granada, 18071 Granada, Spain
2. Centre for Genomics and Oncological Research, Pfizer-University of Granada-Andalusian Regional Government, 18016 Granada, Spain
Interests: bioinformatics and biostatistics; computational biomedicine; omics data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last two decades, bioinformatics has developed and been positioned as an essential discipline for organizing and extracting information from large amounts of biological data. It is a multidisciplinary field that integrates knowledge from biology, computer science, statistics and mathematics. It has had a profound impact on many fields of research, and the development and application of proper bioinformatics methods is a key step in the scientific progress.

In this Special Issue, we will focus on research and review articles on the novel applications and developments of computational biology and bioinformatics, emphasizing those with a strong mathematical background. This is a broad focus, including but not limited to omics data analysis, machine learning applications, systems biology methods and new software packages.

Dr. Jordi Martorell-Marugán
Dr. Pedro Carmona-Sáez
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.

Keywords

  • computational biology
  • bioinformatics
  • statistical genomics
  • omics data analysis
  • mathematical modeling
  • machine learning
  • health data science
  • biostatistics
  • applied mathematics

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Published Papers (4 papers)

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Research

27 pages, 4840 KiB  
Article
Persistent Homology Identifies Pathways Associated with Hepatocellular Carcinoma from Peripheral Blood Samples
by Muhammad Sirajo Abdullahi, Apichat Suratanee, Rosario Michael Piro and Kitiporn Plaimas
Mathematics 2024, 12(5), 725; https://doi.org/10.3390/math12050725 - 29 Feb 2024
Viewed by 1772
Abstract
Topological data analysis (TDA) methods have recently emerged as powerful tools for uncovering intricate patterns and relationships in complex biological data, demonstrating their effectiveness in identifying key genes in breast, lung, and blood cancer. In this study, we applied a TDA technique, specifically [...] Read more.
Topological data analysis (TDA) methods have recently emerged as powerful tools for uncovering intricate patterns and relationships in complex biological data, demonstrating their effectiveness in identifying key genes in breast, lung, and blood cancer. In this study, we applied a TDA technique, specifically persistent homology (PH), to identify key pathways for early detection of hepatocellular carcinoma (HCC). Recognizing the limitations of current strategies for this purpose, we meticulously used PH to analyze RNA sequencing (RNA-seq) data from peripheral blood of both HCC patients and normal controls. This approach enabled us to gain nuanced insights by detecting significant differences between control and disease sample classes. By leveraging topological descriptors crucial for capturing subtle changes between these classes, our study identified 23 noteworthy pathways, including the apelin signaling pathway, the IL-17 signaling pathway, and the p53 signaling pathway. Subsequently, we performed a comparative analysis with a classical enrichment-based pathway analysis method which revealed both shared and unique findings. Notably, while the IL-17 signaling pathway was identified by both methods, the HCC-related apelin signaling and p53 signaling pathways emerged exclusively through our topological approach. In summary, our study underscores the potential of PH to complement traditional pathway analysis approaches, potentially providing additional knowledge for the development of innovative early detection strategies of HCC from blood samples. Full article
(This article belongs to the Special Issue Advanced Computational Biology and Bioinformatics)
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23 pages, 3973 KiB  
Article
Fatigue Estimation Using Peak Features from PPG Signals
by Yi-Xiang Chen, Chin-Kun Tseng, Jung-Tsung Kuo, Chien-Jen Wang, Shu-Hung Chao, Lih-Jen Kau, Yuh-Shyan Hwang and Chun-Ling Lin
Mathematics 2023, 11(16), 3580; https://doi.org/10.3390/math11163580 - 18 Aug 2023
Viewed by 2134
Abstract
Fatigue is a prevalent subjective sensation, affecting both office workers and a significant global population. In Taiwan alone, over 2.6 million individuals—around 30% of office workers—experience chronic fatigue. However, fatigue transcends workplaces, impacting people worldwide and potentially leading to health issues and accidents. [...] Read more.
Fatigue is a prevalent subjective sensation, affecting both office workers and a significant global population. In Taiwan alone, over 2.6 million individuals—around 30% of office workers—experience chronic fatigue. However, fatigue transcends workplaces, impacting people worldwide and potentially leading to health issues and accidents. Gaining insight into one’s fatigue status over time empowers effective management and risk reduction associated with other ailments. Utilizing photoplethysmography (PPG) signals brings advantages due to their easy acquisition and physiological insights. This study crafts a specialized preprocessing and peak detection methodology for PPG signals. A novel fatigue index stems from PPG signals, focusing on the dicrotic peak’s position. This index replaces subjective data from the brief fatigue index (BFI)-Taiwan questionnaire and heart rate variability (HRV) indices derived from PPG signals for assessing fatigue levels. Correlation analysis, involving sixteen healthy adults, highlights a robust correlation (R > 0.53) between the new fatigue index and specific BFI questions, gauging subjective fatigue over the last 24 h. Drawing from these insights, the study computes an average of the identified questions to formulate the evaluated fatigue score, utilizing the newfound fatigue index. The implementation of linear regression establishes a robust fatigue assessment system. The results reveal an impressive 91% correlation coefficient between projected fatigue levels and subjective fatigue experiences. This underscores the remarkable accuracy of the proposed fatigue prediction in evaluating subjective fatigue. This study further operationalized the proposed PPG processing, peak detection method, and fatigue index using C# in a computer environment alongside a PPG device, thereby offering real-time fatigue indices to users. Timely reminders are employed to prompt users to take notice when their index exceeds a predefined threshold, fostering greater attention to their physical well-being. Full article
(This article belongs to the Special Issue Advanced Computational Biology and Bioinformatics)
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10 pages, 503 KiB  
Article
Imputing Phylogenetic Trees Using Tropical Polytopes over the Space of Phylogenetic Trees
by Ruriko Yoshida
Mathematics 2023, 11(15), 3419; https://doi.org/10.3390/math11153419 - 6 Aug 2023
Cited by 2 | Viewed by 1172
Abstract
When we apply comparative phylogenetic analyses to genome data, it poses a significant problem and challenge that some of the given species (or taxa) often have missing genes (i.e., data). In such a case, we have to impute a missing part of a [...] Read more.
When we apply comparative phylogenetic analyses to genome data, it poses a significant problem and challenge that some of the given species (or taxa) often have missing genes (i.e., data). In such a case, we have to impute a missing part of a gene tree from a sample of gene trees. In this short paper, we propose a novel method to infer the missing part of a phylogenetic tree using an analogue of a classical linear regression in the setting of tropical geometry. In our approach, we consider a tropical polytope, a convex hull with respect to the tropical metric closest to the data points. We show a condition that we can guarantee that an estimated tree from the method has at most a Robinson–Foulds (RF) distance of four from the ground truth, and computational experiments with simulated data and empirical data from Clavicipitaceae, which contains more than 4000 genes, show the method works well. Full article
(This article belongs to the Special Issue Advanced Computational Biology and Bioinformatics)
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13 pages, 909 KiB  
Article
Reliable Genetic Correlation Estimation via Multiple Sample Splitting and Smoothing
by The Tien Mai
Mathematics 2023, 11(9), 2163; https://doi.org/10.3390/math11092163 - 4 May 2023
Viewed by 1361
Abstract
In this paper, we aim to investigate the problem of estimating the genetic correlation between two traits. Instead of making assumptions about the distribution of effect sizes of the genetic factors, we propose the use of a high-dimensional linear model to relate a [...] Read more.
In this paper, we aim to investigate the problem of estimating the genetic correlation between two traits. Instead of making assumptions about the distribution of effect sizes of the genetic factors, we propose the use of a high-dimensional linear model to relate a trait to genetic factors. To estimate the genetic correlation, we develop a generic strategy that combines the use of sparse penalization methods and multiple sample splitting approaches. The final estimate is determined by taking the median of the calculations, resulting in a smoothed and reliable estimate. Through simulations, we demonstrate that our proposed approach is reliable and accurate in comparison to naive plug-in methods. To further illustrate the advantages of our method, we apply it to a real-world example of a bacterial GWAS dataset, specifically to estimate the genetic correlation between antibiotic resistant traits in Streptococus pneumoniae. This application not only validates the effectiveness of our method but also highlights its potential in real-world applications. Full article
(This article belongs to the Special Issue Advanced Computational Biology and Bioinformatics)
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