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Mathematics
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Machine Learning in Bioinformatics and Biostatistics
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Machine Learning in Bioinformatics and Biostatistics

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

Deadline for manuscript submissions: 31 August 2024 | Viewed by 4764

Special Issue Editors

Dr. Zulima Fernández-Muñiz

E-Mail Website
Guest Editor
Department of Mathematics, University of Oviedo, 33007 Oviedo, Spain
Interests: inverse problems and parameter reduction; wavelet applications
Dr. Enrique J. DeAndrés-Galiana

E-Mail Website
Guest Editor
Group of Inverse Problems, Optimization and Machine Learning, University of Oviedo, 33003 Oviedo, Spain
Interests: artificial intelligence; bioinformatics; inverse problems

Special Issue Information

Dear Colleagues,

Today, interdisciplinary research is increasingly taking precedence over pure research in a particular field. In this respect, the biological and health sciences are no exception and have incorporated the power of mathematical algorithms into the processing and analysis of biomedical data of various kinds, including medical images, to aid in the diagnosis of certain diseases and the repositioning and development of drugs.

The increasing amount of biomedical data available in accessible and not always well-structured databases requires efficient data storage methods and algorithms to extract the relevant information. This information must be understood by healthcare professionals in order to make decisions that have a direct impact on a good diagnosis focused on avoiding patient suffering, the use of personalized drugs, and the optimisation of resources.

To achieve these objectives, artificial intelligence methods are applied, which combine mathematics and computation and are capable of solving very complicated problems by imitating human reasoning. These methods, which represent statistically real processes, range from machine learning to deep learning, and are applied to all kinds of data for various purposes, including prediction, and therefore require high-quality data.

For this Special Issue, all researchers are encouraged to submit their latest research papers on Machine Learning in Bioinformatics and Biostatistics or related topics that may be of interest and that contribute to a better understanding of the problems by providing solutions to them.

Dr. Zulima Fernández-Muñiz
Dr. Enrique J. DeAndrés-Galiana
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

  • mathematical algorithms
  • biomedical data
  • medical images
  • artificial intelligence methods
  • machine learning
  • deep learning
  • bioinformatics
  • biostatistics

Published Papers (3 papers)

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Research

18 pages, 1518 KiB  
Article
Classification of Motor Imagery Using Trial Extension in Spatial Domain with Rhythmic Components of EEG
by Md. Khademul Islam Molla, Sakir Ahamed, Ahmed M. M. Almassri and Hiroaki Wagatsuma
Mathematics 2023, 11(17), 3801; https://doi.org/10.3390/math11173801 - 04 Sep 2023
Cited by 1 | Viewed by 1970
Abstract
Electrical activities of the human brain can be recorded with electroencephalography (EEG). To characterize motor imagery (MI) tasks for brain–computer interface (BCI) implementation is an easy and cost-effective tool. The MI task is represented by a short-time trial of multichannel EEG. In this [...] Read more.
Electrical activities of the human brain can be recorded with electroencephalography (EEG). To characterize motor imagery (MI) tasks for brain–computer interface (BCI) implementation is an easy and cost-effective tool. The MI task is represented by a short-time trial of multichannel EEG. In this paper, the signal of each channel of raw EEG is decomposed into a finite set of narrowband signals using a Fourier-transformation-based bandpass filter. Rhythmic components of EEG are represented by each of the narrowband signals that characterize the brain activities related to MI tasks. The subband signals are arranged to extend the dimension of the EEG trial in the spatial domain. The spatial features are extracted from the set of extended trials using a common spatial pattern (CSP). An optimum number of features are employed to classify the motor imagery tasks using an artificial neural network. An integrated approach with full-band and narrowband signals is implemented to derive discriminative features for MI classification. In addition, the subject-dependent parameter optimization scheme enhances the performance of the proposed method. The performance evaluation of the proposed method is obtained using two publicly available benchmark datasets (Dataset I and Dataset II). The experimental results in terms of classification accuracy (93.88% with Dataset I and 91.55% with Dataset II) show that it performs better than the recently developed algorithms. The enhanced MI classification accuracy is very much applicable in BCI implementation. Full article
(This article belongs to the Special Issue Machine Learning in Bioinformatics and Biostatistics)
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19 pages, 11560 KiB  
Article
Compressed Sensing Techniques Applied to Medical Images Obtained with Magnetic Resonance
by A. Estela Herguedas-Alonso, Víctor M. García-Suárez and Juan L. Fernández-Martínez
Mathematics 2023, 11(16), 3573; https://doi.org/10.3390/math11163573 - 18 Aug 2023
Viewed by 744
Abstract
The fast and reliable processing of medical images is of paramount importance to adequately generate data to feed machine learning algorithms that can prevent and diagnose health issues. Here, different compressed sensing techniques applied to magnetic resonance imaging are benchmarked as a means [...] Read more.
The fast and reliable processing of medical images is of paramount importance to adequately generate data to feed machine learning algorithms that can prevent and diagnose health issues. Here, different compressed sensing techniques applied to magnetic resonance imaging are benchmarked as a means to reduce the acquisition time spent in the collection of data and signals that form the image. It is shown that by using these techniques, it is possible to reduce the number of signals needed and, therefore, substantially decrease the time to acquire the measurements. To this end, different algorithms are considered and compared: the iterative re-weighted least squares, the iterative soft thresholding algorithm, the iterative hard thresholding algorithm, the primal dual algorithm and the log barrier algorithm. Such algorithms have been implemented in different analysis programs that have been used to perform the reconstruction of the images, and it was found that the iterative soft thresholding algorithm gives the optimal results. It is found that the images obtained with this algorithm have lower quality than the original ones, but in any case, the quality should be good enough to distinguish each body structure and detect any health problems under an expert evaluation and/or statistical analysis. Full article
(This article belongs to the Special Issue Machine Learning in Bioinformatics and Biostatistics)
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16 pages, 6575 KiB  
Article
Three Mathematical Models for COVID-19 Prediction
by Pelayo Martínez-Fernández, Zulima Fernández-Muñiz, Ana Cernea, Juan Luis Fernández-Martínez and Andrzej Kloczkowski
Mathematics 2023, 11(3), 506; https://doi.org/10.3390/math11030506 - 17 Jan 2023
Cited by 5 | Viewed by 1579
Abstract
The COVID-19 outbreak was a major event that greatly impacted the economy and the health systems around the world. Understanding the behavior of the virus and being able to perform long-term and short-term future predictions of the daily new cases is a working [...] Read more.
The COVID-19 outbreak was a major event that greatly impacted the economy and the health systems around the world. Understanding the behavior of the virus and being able to perform long-term and short-term future predictions of the daily new cases is a working field for machine learning methods and mathematical models. This paper compares Verhulst’s, Gompertz´s, and SIR models from the point of view of their efficiency to describe the behavior of COVID-19 in Spain. These mathematical models are used to predict the future of the pandemic by first solving the corresponding inverse problems to identify the model parameters in each wave separately, using as observed data the daily cases in the past. The posterior distributions of the model parameters are then inferred via the Metropolis–Hastings algorithm, comparing the robustness of each prediction model and making different representations to visualize the results obtained concerning the posterior distribution of the model parameters and their predictions. The knowledge acquired is used to perform predictions about the evolution of both the daily number of infected cases and the total number of cases during each wave. As a main conclusion, predictive models are incomplete without a corresponding uncertainty analysis of the corresponding inverse problem. The invariance of the output (posterior prediction) with respect to the forward predictive model that is used shows that the methodology shown in this paper can be used to adopt decisions in real practice (public health). Full article
(This article belongs to the Special Issue Machine Learning in Bioinformatics and Biostatistics)
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