Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
Advanced Computational Biology and Bioinformatics
share announcement

Special Issue "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: 31 May 2024 | Viewed by 2223

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

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 589
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)
►▼ Show Figures

Figure 1

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 - 06 Aug 2023
Viewed by 541
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)
►▼ Show Figures

Figure 1

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 - 04 May 2023
Viewed by 694
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop