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Mathematics
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Bioinformatics, Computational Theory and Intelligent Algorithms
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Bioinformatics, Computational Theory and Intelligent Algorithms

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 720

Special Issue Editors

Prof. Dr. Shu-Chuan (Grace) Chen

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Guest Editor
Department of Mathematics and Statistics, Idaho State University, Pocatello, ID 83209, USA
Interests: bioinformatics; statistical methods and algorithms for functional genomic data
Prof. Dr. Surajit Ray

E-Mail Website
Guest Editor
School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
Interests: the theory and geometry of mixture models; functional data analysis; uncertainty quantification for deep learning algorithms and medical imaging
Dr. Emanuele Zappala

E-Mail Website
Guest Editor
Department of Mathematics and Statistics, Idaho State University, Physical Science Complex, 921 S. 8th Ave., Stop 8085, Pocatello, ID 83209, USA
Interests: machine learning; operator learning; quantum algebra and topology; theoretical and computational physics
Dr. Abhirup Banerjee

E-Mail Website
Guest Editor
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Old Road Campus Research Building, Oxford OX3 7DQ, UK
Interests: biomedical image analysis; AI; machine learning; geometric deep learning; cardiovascular imaging; population health; computational geometry, etc.

Special Issue Information

Dear Colleagues,

Big data have become fundamental in various science, technology, and engineering fields. As our ability to store vast datasets grows, so does the need to develop advanced computational algorithms for analyzing and extracting meaningful information. This necessitates the creation of effective and functional machine learning methods and the computational and numerical approaches to implement them efficiently.

Artificial intelligence applications are vast and far-reaching, impacting and revolutionizing fields like healthcare, engineering, fraud detection, climate science, information science, and more fundamental sciences like physics and chemistry. As such, algorithm development and advances in computational performance are fundamental topics in today's research at the crossroads of applied mathematics, statistics, and computer science.

This Special Issue aims to foster discussions and insights in this multidisciplinary field.

Prof. Dr. Shu-Chuan (Grace) Chen
Prof. Dr. Surajit Ray
Dr. Emanuele Zappala
Dr. Abhirup Banerjee
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

  • artificial intelligence
  • machine learning
  • computational algorithms
  • bioinformatics
  • big data

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Published Papers (2 papers)

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Research

11 pages, 263 KiB  
Article
Application of Non-Sparse Manifold Regularized Multiple Kernel Classifier
by Tao Yang
Mathematics 2025, 13(7), 1050; https://doi.org/10.3390/math13071050 - 24 Mar 2025
Viewed by 200
Abstract
Non-sparse multiple kernel learning is efficient but not directly able to be applied in a semi-supervised scenario; therefore, we extend it to semi-supervised learning by using a manifold regularization. The manifold regularization is based on a graph constructed on all the data samples [...] Read more.
Non-sparse multiple kernel learning is efficient but not directly able to be applied in a semi-supervised scenario; therefore, we extend it to semi-supervised learning by using a manifold regularization. The manifold regularization is based on a graph constructed on all the data samples including the labeled and the unlabeled, and forces the regularized classifier smooth along the graph. In this study, we propose the manifold regularized p-norm multiple kernels model and provide the solutions with proofs. The risk bound is briefly introduced based on the local Rademacher complexity. Experiments on several datasets and comparisons with several methods show that the efficiency of the proposed model to be used in semi-supervised scenario. Full article
(This article belongs to the Special Issue Bioinformatics, Computational Theory and Intelligent Algorithms)
22 pages, 11281 KiB  
Article
Splitting and Merging for Active Contours: Plug-and-Play
by Mojtaba Lashgari, Abhirup Banerjee and Hossein Rabbani
Mathematics 2025, 13(6), 991; https://doi.org/10.3390/math13060991 - 18 Mar 2025
Viewed by 212
Abstract
This study tackles the challenge of splitting and merging in parametric active contours or snakes. The proposed method comprises three stages: (1) fully 4-connected interpolation, (2) snake splitting, and (3) snakes merging. For this purpose, first, the coordinates of snake points are separated [...] Read more.
This study tackles the challenge of splitting and merging in parametric active contours or snakes. The proposed method comprises three stages: (1) fully 4-connected interpolation, (2) snake splitting, and (3) snakes merging. For this purpose, first, the coordinates of snake points are separated into two corrupted 1D signals, with missing X/Y samples in the signals representing missing snakes’ coordinates. These missing X/Y samples are estimated using a constrained Tikhonov regularisation model, ensuring fully 4-connected snakes. Next, crossing points are identified by plotting snake points onto a raster matrix, detecting overlaps where multiple snake points occupy the same raster cell. Finally, snakes are split or merged by extracting snake points between crossing snake points that form a loop using a heuristic approach. Experimental results on the boundary detection of enamel in Micro-CT images and coronary arteries’ lumen in CT images demonstrate the proposed method’s ability to handle contour splitting and merging effectively. Full article
(This article belongs to the Special Issue Bioinformatics, Computational Theory and Intelligent Algorithms)
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