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Special Issues
Bioinformatics and Big Data Challenges in Personalized Medicine
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Special Issue "Bioinformatics and Big Data Challenges in Personalized Medicine"

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: 26 April 2024 | Viewed by 1196

Special Issue Editors

Dr. Amir Hajjam El Hassani

E-Mail Website
Guest Editor
1. Deputy Director of Nanomedicine Lab., Imagery & Therapeutics of Université de Franche Comté (UFC), Besançon, France
2. Research Team Leader of Health Systems Organization, Besançon, France
Interests: big data; artificial intelligence; deep learning; reinforcement learning; eHealth information processing; combinatorial optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Younes Jabrane

E-Mail Website
Guest Editor
MSC Laboratory, Cadi Ayyad University, Marakech, Morocco
Interests: image processing; machine learning; data analysis

Special Issue Information

Dear Colleagues,

In recent years, the development of high-throughput, data-intensive biomedical research assays and technologies has generated massive amounts of data, posing challenges in terms of data integration, analysis, and interpretation. The results of these biomedical research assays often reveal a great heterogeneity in the pathophysiologic factors and processes that contribute to diseases. This suggests that there is a need to tailor medicines to the nuanced and often unique features possessed by individual patients. In this context, the use of artificial intelligence (AI) techniques has shown great promise in addressing the challenges of personalized medicine, despite there being some limitations to such techniques.

The aim of the present Special Issue is to collect original research articles and reviews that will provide updates and future perspectives regarding the application of AI techniques in the field of bioinformatics for personalized medicine.

Dr. Amir Hajjam El Hassani
Dr. Younes Jabrane
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. Journal of Personalized Medicine 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 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

  • machine learning
  • biomarker
  • deep learning
  • imaging
  • data analysis
  • bioinformatics

Published Papers (1 paper)

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Research

23 pages, 4890 KiB  
Article
Medical Image Segmentation Using Automatic Optimized U-Net Architecture Based on Genetic Algorithm
by
Mohammed Khouy
,
Younes Jabrane
,
Mustapha Ameur
and
Amir Hajjam El Hassani
J. Pers. Med. 2023, 13(9), 1298; https://doi.org/10.3390/jpm13091298 - 25 Aug 2023
Viewed by 927
Abstract
Image segmentation is a crucial aspect of clinical decision making in medicine, and as such, it has greatly enhanced the sustainability of medical care. Consequently, biomedical image segmentation has become a prominent research area in the field of computer vision. With the advent [...] Read more.
Image segmentation is a crucial aspect of clinical decision making in medicine, and as such, it has greatly enhanced the sustainability of medical care. Consequently, biomedical image segmentation has become a prominent research area in the field of computer vision. With the advent of deep learning, many manual design-based methods have been proposed and have shown promising results in achieving state-of-the-art performance in biomedical image segmentation. However, these methods often require significant expert knowledge and have an enormous number of parameters, necessitating substantial computational resources. Thus, this paper proposes a new approach called GA-UNet, which employs genetic algorithms to automatically design a U-shape convolution neural network with good performance while minimizing the complexity of its architecture-based parameters, thereby addressing the above challenges. The proposed GA-UNet is evaluated on three datasets: lung image segmentation, cell nuclei segmentation in microscope images (DSB 2018), and liver image segmentation. Interestingly, our experimental results demonstrate that the proposed method achieves competitive performance with a smaller architecture and fewer parameters than the original U-Net model. It achieves an accuracy of 98.78% for lung image segmentation, 95.96% for cell nuclei segmentation in microscope images (DSB 2018), and 98.58% for liver image segmentation by using merely 0.24%, 0.48%, and 0.67% of the number of parameters in the original U-Net architecture for the lung image segmentation dataset, the DSB 2018 dataset, and the liver image segmentation dataset, respectively. This reduction in complexity makes our proposed approach, GA-UNet, a more viable option for deployment in resource-limited environments or real-world implementations that demand more efficient and faster inference times. Full article
(This article belongs to the Special Issue Bioinformatics and Big Data Challenges in Personalized Medicine)
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