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Special Issues
Bioinformatics and Medicine: 2nd Edition
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Bioinformatics and Medicine: 2nd Edition

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

Deadline for manuscript submissions: closed (25 February 2025) | Viewed by 2168

Special Issue Editors

Prof. Dr. Dorota Bielińska-Wąż

E-Mail Website
Guest Editor
Department of Radiological Informatics and Statistics, Medical University of Gdańsk, 80-210 Gdańsk, Poland
Interests: bioinformatics; graphical representations of biological sequences; biophysics; mathematical modeling in biomedical and social sciences; health and biomedical informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Piotr Wąż

E-Mail Website
Guest Editor
Faculty of Health Sciences, Medical University of Gdańsk, 80-210 Gdańsk, Poland
Interests: bioinformatics; graphical representations of biological sequences; computational statistics; mathematical modeling in medicine, physics, astronomy; computational pharmacology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

We are launching a Special Issue, titled “Bioinformatics and Medicine: 2nd Edition”, and are looking to publish original research, reviews, and combined original–review papers. This Special Issue is mainly devoted to a branch of bioinformatics known as “alignment-free bioinformatics methods”. This branch of bioinformatics, developed over the last two decades, is one of the most promising directions in the development of this area of science. In particular, articles on graphical representation methods, aimed at both the graphical and numerical analysis of the similarity/dissimilarity of biological sequences (DNA, RNA, and protein), are welcome. The submitted articles may contain descriptions of new algorithms; papers dealing with different aspects of graphical or numerical comparisons of the considered objects or focused on discussing a variety of applications of the methods already published in biomedical sciences are also welcome. We will also accept papers related to standard bioinformatics methods and medical informatics.

The first edition: “Bioinformatics and Medicine”: https://www.mdpi.com/journal/jpm/special_issues/med_bioinformat

Prof. Dr. Dorota Bielińska-Wąż
Prof. Dr. Piotr Wąż
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

  • bioinformatics
  • alignment-free bioinformatics methods
  • graphical bioinformatics
  • biomedical informatics
  • data analysis
  • mathematical modeling

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Related Special Issue

Published Papers (1 paper)

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Research

16 pages, 521 KiB  
Article
Predicting Response to [177Lu]Lu-PSMA Therapy in mCRPC Using Machine Learning
by Kaiyuan Gong, Baptiste Magnier, Salomé L’hostis, Fanny Borrely, Sébastien Le Bon, Nadine Houede, Adel Mamou, Laurent Maimoun, Pierre Olivier Kotzki and Vincent Boudousq
J. Pers. Med. 2024, 14(11), 1068; https://doi.org/10.3390/jpm14111068 - 23 Oct 2024
Viewed by 1709
Abstract
Background/Objectives: Radioligandtherapy (RLT) with [177Lu]Lu-PSMA has been newly introduced as a routine treatment for metastatic castration-resistant prostate cancer (mCRPC). However, not all patients can tolerate the entire therapeutic sequence, and in some cases, the treatment may prove ineffective. In real-world conditions, the aim [...] Read more.
Background/Objectives: Radioligandtherapy (RLT) with [177Lu]Lu-PSMA has been newly introduced as a routine treatment for metastatic castration-resistant prostate cancer (mCRPC). However, not all patients can tolerate the entire therapeutic sequence, and in some cases, the treatment may prove ineffective. In real-world conditions, the aim is to distinguish between patients who fully benefit from treatment (those who respond effectively and tolerate the entire therapeutic sequence) and those who do not respond or cannot tolerate the entire sequence. This study explores predictive factors to distinguish between fully beneficial RLT treatment patients (FBTP) and not fully beneficial RLT treatment patients (NFBTP). The objective was to enhance the understanding of predictive factors influencing RLT effectiveness and to highlight the significance of machine learning in optimizing patient selection for treatment planning. Methods: Data from 25 mCRPC patients, categorized as FBTP (11) or NFBTP (14) to RLT, were analyzed. The dataset included clinical, imaging, and biological parameters. Data analysis techniques, including exploratory data analysis and feature engineering, were used to develop machine learning models for predicting patient outcomes. Results: Imaging data analysis revealed statistically significant differences in the renal uptake intensity of Choline between the two groups. A discordance of FDG+ and PSMA− was identified as a potential indicator of NFBTP. The integration of biological data enhanced the model’s predictive capability, achieving an accuracy of 0.92, a sensitivity of 0.96, and a precision of 0.96. Adding blood parameters like neutrophils, leukocytes, and alkaline phosphatase greatly increased prediction accuracy. Conclusions: This study emphasizes the significance of an integrated approach that merges imaging and biological data, thereby augmenting the predictive accuracy of patient outcomes in RLT with [177Lu]Lu-PSMA. In particular, including Choline PET among the imaging parameters provides unique insights into the predictive factors affecting RLT efficacy. This approach not only deepens the understanding of predictive factors but also underscores the utility of machine learning in refining the patient selection process for optimized treatment planning. Full article
(This article belongs to the Special Issue Bioinformatics and Medicine: 2nd Edition)
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