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Application of Deep and Machine Learning in Personalized Medicine and Individualized Bioinstruments

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

Deadline for manuscript submissions: 25 July 2024 | Viewed by 4804

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Special Issue Editor

Dr. Mohsen Annabestani

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Guest Editor

Weill Cornell Medicine, New York, NY, USA
Interests: biomedical engineering and artificial intelligence

Special Issue Information

Dear Colleagues,

In recent years, the field of healthcare has witnessed remarkable advancements through the convergence of deep learning and machine learning techniques with personalized medicine and the development of individualized bioinstruments. This exciting intersection has paved the way for transformative approaches in diagnostics, treatment selection, monitoring, and patient care. This Special Issue aims to explore the latest research and innovations in the application of deep and machine learning in personalized medicine, while also highlighting the development of individualized bioinstruments for precision healthcare.

The concept of personalized medicine has emerged as a paradigm shift in healthcare, recognizing that each individual has unique genetic variations, environmental exposures, and lifestyle factors that influence disease susceptibility and treatment response. Genomic pre-trained networks and language models for genomics have revolutionized our ability to analyze and interpret complex genetic data, enabling the identification of disease-associated variants, gene expression patterns, and novel therapeutic targets. Moreover, omics and multi-omics intelligent data analysis have provided comprehensive insights into molecular signatures, biomarkers, and disease pathways, fostering a deeper understanding of individualized disease mechanisms.

Simultaneously, the development of individualized bioinstruments has played a pivotal role in precision healthcare. AI-reinforced wearable sensors, intelligent point-of-care tests, and AI for lab-on-a-chip platforms have enabled real-time monitoring, rapid diagnostics, and personalized treatment decisions at the point of need. Deep-learning-assisted microfluidics have revolutionized the manipulation of tiny volumes of fluids with a high precision, enabling advanced diagnostic and therapeutic applications. Furthermore, smart personalized prosthetics, personalized smart garments, and intelligent wound healing patches have transformed the landscape of patient care, offering customized solutions tailored to individual needs.

This Special Issue aims to provide a platform for researchers to present their groundbreaking research, showcasing novel methodologies, innovative applications, and significant advancements in the realm of deep and machine learning in personalized medicine and the development of individualized bioinstruments. We welcome contributions that highlight the seamless integration of computational approaches and bioinstrumentation, offering promising solutions to the challenges of personalized healthcare. Researchers from diverse disciplines are invited to submit their original research, review papers, and short communications, exploring a wide range of topics within this domain. We encourage submissions that cover various aspects, including, but not limited to, the following topics.

Personalized medicine:

Genomic pre-trained networks;
Language models for genomics;
Omics and multi-omics intelligent data analysis;
Generative AI for artificial genomes;
Intelligent biomarker identification;
AI-based clinical decision support;
Predictive ML for drug response.

Individualized Bio instruments:

AI-reinforced wearable sensors;
Intelligent point-of-care tests;
AI for lab-on-a-chip (AI-on-a-chip);
Deep-learning-assisted microfluidics;
Smart personalized prosthetics;
Personalized smart garments;
Intelligent wound healing patches.

Dr. Mohsen Annabestani
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. 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

intelligent medical devices
wearable biosensors
AI
machine learning
deep learning
Personalized medicine

Published Papers (3 papers)

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Research

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16 pages, 3885 KiB

Open AccessArticle

HBCR_DMR: A Hybrid Method Based on Beta-Binomial Bayesian Hierarchical Model and Combination of Ranking Method to Detect Differential Methylation Regions in Bisulfite Sequencing Data

by Maryam Yassi, Ehsan Shams Davodly, Saeedeh Hajebi Khaniki and Mohammad Amin Kerachian

J. Pers. Med. 2024, 14(4), 361; https://doi.org/10.3390/jpm14040361 - 29 Mar 2024

Viewed by 689

Abstract

DNA methylation is a key epigenetic modification involved in gene regulation, contributing to both physiological and pathological conditions. For a more profound comprehension, it is essential to conduct a precise comparison of DNA methylation patterns between sample groups that represent distinct statuses. Analysis of differentially methylated regions (DMRs) using computational approaches can help uncover the precise relationships between these phenomena. This paper describes a hybrid model that combines the beta-binomial Bayesian hierarchical model with a combination of ranking methods known as HBCR_DMR. During the initial phase, we model the actual methylation proportions of the CpG sites (CpGs) within the replicates. This modeling is achieved through beta-binomial distribution, with parameters set by a group mean and a dispersion parameter. During the second stage, we establish the selection of distinguishing CpG sites based on their methylation status, employing multiple ranking techniques. Finally, we combine the ranking lists of differentially methylated CpG sites through a voting system. Our analyses, encompassing simulations and real data, reveal outstanding performance metrics, including a sensitivity of 0.72, specificity of 0.89, and an F1 score of 0.76, yielding an overall accuracy of 0.82 and an AUC of 0.94. These findings underscore HBCR_DMR’s robust capacity to distinguish methylated regions, confirming its utility as a valuable tool for DNA methylation analysis. Full article

(This article belongs to the Special Issue Application of Deep and Machine Learning in Personalized Medicine and Individualized Bioinstruments)

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Review

Jump to: Research

21 pages, 2042 KiB

Open AccessReview

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

by Ali Olyanasab and Mohsen Annabestani

J. Pers. Med. 2024, 14(2), 203; https://doi.org/10.3390/jpm14020203 - 13 Feb 2024

Viewed by 2146

Abstract

This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer’s physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual’s physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being. Full article

(This article belongs to the Special Issue Application of Deep and Machine Learning in Personalized Medicine and Individualized Bioinstruments)

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26 pages, 3538 KiB

Open AccessReview

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine

by Mohammadhossein Salimi, Majid Roshanfar, Nima Tabatabaei and Bobak Mosadegh

J. Pers. Med. 2024, 14(1), 33; https://doi.org/10.3390/jpm14010033 - 26 Dec 2023

Viewed by 1498

Abstract

Personalized medicine transforms healthcare by adapting interventions to individuals’ unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine. Full article

(This article belongs to the Special Issue Application of Deep and Machine Learning in Personalized Medicine and Individualized Bioinstruments)

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