Editor's Choices Series for Methods in Biomedical Informatics Section

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Methods in Biomedical Informatics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 419

Special Issue Editor


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Guest Editor
LS2_10—Bioinformatics, Università degli Studi di Verona, 37129 Verona, Italy
Interests: bioinformatics; computational biology; medical imaging analysis; artificial intelligence; machine learning; data analysis; personalized medicine; predictive modeling; healthcare innovation; methodological advancements
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Special Issue Information

Dear Colleagues,

The Editor's Choice Series for Methods in Biomedical Informatics Section presents an insightful compilation of cutting-edge methodologies pivotal in the convergence of biomedical science and informatics. This curated selection encompasses an array of innovative techniques, tools, and approaches instrumental in advancing biomedical research, clinical practice, and healthcare innovation.

Spanning diverse domains such as bioinformatics, computational biology, medical imaging analysis, artificial intelligence, and machine learning, this series delves into the methodologies reshaping the landscape of informatics in healthcare. From sophisticated algorithms for data analysis to pioneering strategies in personalized medicine and predictive modeling, this collection encapsulates the dynamic evolution of the methods driving transformative impacts.

Please note that this series does not accept submissions of brief reports, but focuses on the in-depth exploration and analysis of methodologies and their application in the biomedical informatics domain.

Prof. Dr. Rosalba Giugno
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. BioMedInformatics is an international peer-reviewed open access quarterly 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 1000 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

  • bioinformatics
  • computational biology
  • medical imaging analysis
  • artificial intelligence
  • machine learning
  • data analysis
  • personalized medicine
  • predictive modeling
  • healthcare innovation
  • methodological advancements

Published Papers (1 paper)

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Research

11 pages, 3190 KiB  
Article
Assaying and Classifying T Cell Function by Cell Morphology
by Xin Wang, Stacey M. Fernandes, Jennifer R. Brown and Lance C. Kam
BioMedInformatics 2024, 4(2), 1144-1154; https://doi.org/10.3390/biomedinformatics4020063 - 26 Apr 2024
Viewed by 297
Abstract
Immune cell function varies tremendously between individuals, posing a major challenge to emerging cellular immunotherapies. This report pursues the use of cell morphology as an indicator of high-level T cell function. Short-term spreading of T cells on planar, elastic surfaces was quantified by [...] Read more.
Immune cell function varies tremendously between individuals, posing a major challenge to emerging cellular immunotherapies. This report pursues the use of cell morphology as an indicator of high-level T cell function. Short-term spreading of T cells on planar, elastic surfaces was quantified by 11 morphological parameters and analyzed to identify effects of both intrinsic and extrinsic factors. Our findings identified morphological features that varied between T cells isolated from healthy donors and those from patients being treated for Chronic Lymphocytic Leukemia (CLL). This approach also identified differences between cell responses to substrates of different elastic modulus. Combining multiple features through a machine learning approach such as Decision Tree or Random Forest provided an effective means for identifying whether T cells came from healthy or CLL donors. Further development of this approach could lead to a rapid assay of T cell function to guide cellular immunotherapy. Full article
(This article belongs to the Special Issue Editor's Choices Series for Methods in Biomedical Informatics Section)
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