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Symmetry
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Machine Learning and Symmetry Numerical Analysis in Biomedical Informatics
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Machine Learning and Symmetry Numerical Analysis in Biomedical Informatics

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 8021

Special Issue Editor

Dr. Guangming Zhang

E-Mail Website
Guest Editor
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Interests: computational surgery; medical image analysis; biomechanical analysis; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With recent advancements in medical and health informatics, disease and treatment research data have grown exponentially, and the nature of such big data is increasingly complex. There are many advantages to be gained with the application in healthcare of advances in machine learning and big data; however, there are many challenges in providing systems accurate enough to be useful to clinicians and patients. Not only are large amounts of data available, but sensitivity and specificity must be given special attention, as well as ensuring support systems fit rationally into the health system. Recently, machine learning and symmetry numerical analysis have been applied for effectively diagnosing and predicting diseases from healthcare data and biomedical data. Intelligent assisted computational models of user information preferences and interaction behaviors in biomedical informatics can also be designed. The aim of this Special Issue is to gather new advances in the use of machine learning and symmetry numerical analysis in biomedical informatics. We welcome both original research and review articles.

Dr. Guangming Zhang
Guest Editor

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. Symmetry is an international peer-reviewed open access monthly 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 2400 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

  • symmetry versus asymmetry in medical image analysis
  • big data
  • machine learning
  • CT, MRI, X-rays
  • symmetry numerical analysis
  • healthcare informatics
  • symmetry versus asymmetry in biomedical data
  • cluster and classification
  • effects of symmetry or asymmetric load in biomechanical analysis

Published Papers (2 papers)

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Review

33 pages, 1500 KiB  
Review
Hybrid Feature Selection of Breast Cancer Gene Expression Microarray Data Based on Metaheuristic Methods: A Comprehensive Review
by Nursabillilah Mohd Ali, Rosli Besar and Nor Azlina Ab. Aziz
Symmetry 2022, 14(10), 1955; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14101955 - 20 Sep 2022
Cited by 7 | Viewed by 1853
Abstract
Breast cancer (BC) remains the most dominant cancer among women worldwide. Numerous BC gene expression microarray-based studies have been employed in cancer classification and prognosis. The availability of gene expression microarray data together with advanced classification methods has enabled accurate and precise classification. [...] Read more.
Breast cancer (BC) remains the most dominant cancer among women worldwide. Numerous BC gene expression microarray-based studies have been employed in cancer classification and prognosis. The availability of gene expression microarray data together with advanced classification methods has enabled accurate and precise classification. Nevertheless, the microarray datasets suffer from a large number of gene expression levels, limited sample size, and irrelevant features. Additionally, datasets are often asymmetrical, where the number of samples from different classes is not balanced. These limitations make it difficult to determine the actual features that contribute to the existence of cancer classification in the gene expression profiles. Various accurate feature selection methods exist, and they are being widely applied. The objective of feature selection is to search for a relevant, discriminant feature subset from the basic feature space. In this review, we aim to compile and review the latest hybrid feature selection methods based on bio-inspired metaheuristic methods and wrapper methods for the classification of BC and other types of cancer. Full article
(This article belongs to the Special Issue Machine Learning and Symmetry Numerical Analysis in Biomedical Informatics)
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28 pages, 1226 KiB  
Review
A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions
by Talal A. A. Abdullah, Mohd Soperi Mohd Zahid and Waleed Ali
Symmetry 2021, 13(12), 2439; https://0-doi-org.brum.beds.ac.uk/10.3390/sym13122439 - 17 Dec 2021
Cited by 31 | Viewed by 5299
Abstract
We have witnessed the impact of ML in disease diagnosis, image recognition and classification, and many more related fields. Healthcare is a sensitive field related to people’s lives in which decisions need to be carefully taken based on solid evidence. However, most ML [...] Read more.
We have witnessed the impact of ML in disease diagnosis, image recognition and classification, and many more related fields. Healthcare is a sensitive field related to people’s lives in which decisions need to be carefully taken based on solid evidence. However, most ML models are complex, i.e., black-box, meaning they do not provide insights into how the problems are solved or why such decisions are proposed. This lack of interpretability is the main reason why some ML models are not widely used yet in real environments such as healthcare. Therefore, it would be beneficial if ML models could provide explanations allowing physicians to make data-driven decisions that lead to higher quality service. Recently, several efforts have been made in proposing interpretable machine learning models to become more convenient and applicable in real environments. This paper aims to provide a comprehensive survey and symmetry phenomena of IML models and their applications in healthcare. The fundamental characteristics, theoretical underpinnings needed to develop IML, and taxonomy for IML are presented. Several examples of how they are applied in healthcare are investigated to encourage and facilitate the use of IML models in healthcare. Furthermore, current limitations, challenges, and future directions that might impact applying ML in healthcare are addressed. Full article
(This article belongs to the Special Issue Machine Learning and Symmetry Numerical Analysis in Biomedical Informatics)
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