Recent Trends in Artificial Learning and Data Processing for Biomedical Engineering

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 1867

Special Issue Editors


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Guest Editor
LIASD Research Lab. – University of Paris 8, 2 Rue de la Liberté, 93526 Saint-Denis, France
Interests: robotics; soft computing; BCI; WSN; biometrics
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Special Issue Information

Dear Colleagues,

This Special Issue aims to solicit original research papers focusing on novel solutions to challenging problems in biomedical engineering using artificial learning and advanced data processing algorithms and methods.

We are inviting original research works covering novel theories, innovative methods, and meaningful applications that can potentially lead to significant advances in the biomedical engineering field.

The main topics of interest include but are not limited to:

  • Biomedical signal processing;
  • Medical and biological imaging;
  • Pattern recognition algorithms and methods;
  • Artificial learning algorithms and methods (e.g., machine learning, deep learning, statistical learning);
  • Applications of artificial intelligence in biomedical engineering;
  • Healthcare applications (e.g., detection, diagnostic, therapeutic, e-health, m-health);
  • Healthcare Internet of Things;
  • Smart Healthcare;
  • Decision support systems in biomedical engineering;
  • Neural engineering;
  • Clinical engineering;
  • Rehabilitation engineering;
  • Biological engineering;
  • Biomedical sensors and devices;
  • Biomedical wearable technology;
  • Related applications.

Prof. Dr. Larbi Boubchir
Prof. Dr. Boubaker Daachi 
Guest Editors

Manuscript Submission Information

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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. Electronics 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 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

  • image processing
  • pattern recognition
  • artificial intelligence
  • machine learning
  • feature engineering
  • biomedical engineering
  • healthcare

Published Papers (1 paper)

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Research

15 pages, 5290 KiB  
Article
An Empirical Mode Decomposition-Based Method to Identify Topologically Associated Domains from Chromatin Interactions
by Xuemin Zhao, Ran Duan and Shaowen Yao
Electronics 2023, 12(19), 4154; https://doi.org/10.3390/electronics12194154 - 06 Oct 2023
Cited by 1 | Viewed by 914
Abstract
Topologically associated domains (TADs) represent essential units constituting chromatin’s intricate three-dimensional spatial organization. TADs are stably present across cell types and species, and their influence on vital biological processes, such as gene expression, DNA replication, and chromosomal translocation, underscores their significance. Accordingly, the [...] Read more.
Topologically associated domains (TADs) represent essential units constituting chromatin’s intricate three-dimensional spatial organization. TADs are stably present across cell types and species, and their influence on vital biological processes, such as gene expression, DNA replication, and chromosomal translocation, underscores their significance. Accordingly, the identification of TADs within the Hi-C interaction matrix is a key point in three-dimensional genomics. TADs manifest as contiguous blocks along the diagonal of the Hi-C interaction matrix, which are characterized by dense interactions within blocks and sparse interactions between blocks. An optimization method is proposed to enhance Hi-C interaction matrix data using the empirical mode decomposition method, which requires no prior knowledge and adaptively decomposes Hi-C data into a sum of multiple eigenmodal functions via exploiting the inherent characteristics of variations in the input Hi-C data. We identify TADs within the optimized data and compared the results with five commonly used TAD detection methods, namely the Directionality Index (DI), Interaction Isolation (IS), HiCKey, HiCDB, and TopDom. The results demonstrate the universality and efficiency of the proposed method, highlighting its potential as a valuable tool in TAD identification. Full article
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