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Mathematics
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Artificial Intelligence for Biomedical Applications
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Artificial Intelligence for Biomedical Applications

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3476

Special Issue Editors

Prof. Dr. Cheng-Hong Yang

E-Mail Website
Guest Editor
Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
Interests: artificial intelligence; smart computing; machine learning; data mining; big data; image recognition; embedded system development; medical engineering
Dr. Yu-Da Lin

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu 880011, Taiwan
Interests: algorithm design; machine learning; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) refers to systems or machines that can perform tasks by imitating human intelligence, and continuously adjust and evolve based on the information collected. Rather than a specific format or function, AI focuses on the process and ability of super-mind and data analysis. AI is fast becoming the cornerstone of innovation. Through various machine learning techniques, it is possible to identify patterns of information and make predictions. AI is advancing biomedical science in many ways, from basic PCR primer design to improving image-based diagnostics, engineering strategies to improve exercise related to injury, birth defects, or neurological or cardiovascular disease, and predicting behavior and nerve response to stimuli. Mathematics is compiling a Special Issue highlighting the latest applications of AI in biomedical sciences, and invites you to submit your research for consideration. The topics of interest include, but are not limited to:

  • Artificial intelligence technologies for biomedical sciences and biomedical engineering;
  • Biomedical image reconstruction algorithms;
  • Evolutionary computation in biomedical applications;
  • Machine learning-based biomedical systems;
  • Mathematical modeling for biomedical problems;
  • Mathematical and statistical analysis for biomedical applications;
  • Computational methods and optimization technologies in solving biomedical problems.

Prof. Dr. Cheng-Hong Yang
Dr. Yu-Da Lin
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.

Published Papers (3 papers)

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Research

18 pages, 3282 KiB  
Article
Deep-Representation-Learning-Based Classification Strategy for Anticancer Peptides
by Shujaat Khan
Mathematics 2024, 12(9), 1330; https://doi.org/10.3390/math12091330 - 27 Apr 2024
Viewed by 425
Abstract
Cancer, with its complexity and numerous origins, continues to provide a huge challenge in medical research. Anticancer peptides are a potential treatment option, but identifying and synthesizing them on a large scale requires accurate prediction algorithms. This study presents an intuitive classification strategy, [...] Read more.
Cancer, with its complexity and numerous origins, continues to provide a huge challenge in medical research. Anticancer peptides are a potential treatment option, but identifying and synthesizing them on a large scale requires accurate prediction algorithms. This study presents an intuitive classification strategy, named ACP-LSE, based on representation learning, specifically, a deep latent-space encoding scheme. ACP-LSE can demonstrate notable advancements in classification outcomes, particularly in scenarios with limited sample sizes and abundant features. ACP-LSE differs from typical black-box approaches by focusing on representation learning. Utilizing an auto-encoder-inspired network, it embeds high-dimensional features, such as the composition of g-spaced amino acid pairs, into a compressed latent space. In contrast to conventional auto-encoders, ACP-LSE ensures that the learned feature set is both small and effective for classification, giving a transparent alternative. The suggested approach is tested on benchmark datasets and demonstrates higher performance compared to the current methods. The results indicate improved Matthew’s correlation coefficient and balanced accuracy, offering insights into crucial aspects for developing new ACPs. The implementation of the proposed ACP-LSE approach is accessible online, providing a valuable and reproducible resource for researchers in the field. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Applications)
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11 pages, 1335 KiB  
Article
Active Labeling Correction of Mealtimes and the Appearance of Types of Carbohydrates in Type 1 Diabetes Information Records
by Ivan Contreras, Mario Muñoz-Organero, Aleix Beneyto and Josep Vehi
Mathematics 2023, 11(19), 4050; https://doi.org/10.3390/math11194050 - 24 Sep 2023
Viewed by 811
Abstract
People with type 1 diabetes are required to adhere to their treatment rigorously to ensure maximum benefits. Diabetes tracking tools have played an important role in this regard. Type 1 diabetes monitoring has evolved and matured with the advent of blood glucose monitor [...] Read more.
People with type 1 diabetes are required to adhere to their treatment rigorously to ensure maximum benefits. Diabetes tracking tools have played an important role in this regard. Type 1 diabetes monitoring has evolved and matured with the advent of blood glucose monitor sensors, insulin pens, and insulin pump automation. However, carbohydrate monitoring has seen little progress despite carbohydrates representing a major potential disruption. Relying on the modeling of carbohydrate intake using the rate of exogenous glucose appearance, we first present a methodology capable of identifying the type of carbohydrates ingested by classifying them into fast and non-fast carbohydrates. Second, we test the ability of the methodology to identify the correct synchrony between the actual mealtime and the time labeled as such in diabetes records. A deep neural network is trained with processed input data that consist of different values to estimate the parameters in a series of experiments in which, firstly, we vary the response of ingested carbohydrates for subsequent identification and, secondly, we shift the learned carbohydrate absorption curves in time to estimate when the meals were administered to virtual patients. This study validates that the identification of different carbohydrate classes in the meal records of people with type 1 diabetes could become a valuable source of information, as it demonstrates the potential to identify inaccuracies in the recorded meal records of these patients, suggesting the potential abilities of the next generation of type 1 diabetes management tools. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Applications)
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17 pages, 3560 KiB  
Article
Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro Fertilization
by Muhammad Ishaq, Salman Raza, Hunza Rehar, Shan e Zain ul Abadeen, Dildar Hussain, Rizwan Ali Naqvi and Seung-Won Lee
Mathematics 2023, 11(9), 2023; https://doi.org/10.3390/math11092023 - 24 Apr 2023
Cited by 1 | Viewed by 1470
Abstract
The increasing global infertility rate is a matter of significant concern. In vitro fertilization (IVF) significantly minimizes infertility by providing an alternative clinical means of becoming pregnant. The success of IVF mainly depends on the assessment and analysis of human blastocyst components such [...] Read more.
The increasing global infertility rate is a matter of significant concern. In vitro fertilization (IVF) significantly minimizes infertility by providing an alternative clinical means of becoming pregnant. The success of IVF mainly depends on the assessment and analysis of human blastocyst components such as the blastocoel (BC), zona pellucida (ZP), inner cell mass (ICM), and trophectoderm (TE). Embryologists perform a morphological assessment of the blastocyst components for the selection of potential embryos to be used in the IVF process. Manual assessment of blastocyst components is time-consuming, subjective, and prone to errors. Therefore, artificial intelligence (AI)-based methods are highly desirable for enhancing the success rate and efficiency of IVF. In this study, a novel feature-supplementation-based blastocyst segmentation network (FSBS-Net) has been developed to deliver higher segmentation accuracy for blastocyst components with less computational overhead compared with state-of-the-art methods. FSBS-Net uses an effective feature supplementation mechanism along with ascending channel convolutional blocks to accurately detect the pixels of the blastocyst components with minimal spatial loss. The proposed method was evaluated using an open database for human blastocyst component segmentation, and it outperformed state-of-the-art methods in terms of both segmentation accuracy and computational efficiency. FSBS-Net segmented the BC, ZP, ICM, TE, and background with intersections over union (IoU) values of 89.15, 85.80, 85.55, 80.17, and 95.61%, respectively. In addition, FSBS-Net achieved a mean IoU for all categories of 87.26% with only 2.01 million trainable parameters. The experimental results demonstrate that the proposed method could be very helpful in assisting embryologists in the morphological assessment of human blastocyst components. Full article
(This article belongs to the Special Issue Artificial Intelligence for Biomedical Applications)
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