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

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 April 2025) | Viewed by 24180

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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13 pages, 2441 KiB  
Article
An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction
by Hui-O Chen, Yuan-Chi Cui, Peng-Chan Lin and Jung-Hsien Chiang
J. Pers. Med. 2024, 14(7), 694; https://doi.org/10.3390/jpm14070694 - 27 Jun 2024
Cited by 2 | Viewed by 1876
Abstract
By using omics, we can now examine all components of biological systems simultaneously. Deep learning-based drug prediction methods have shown promise by integrating cancer-related multi-omics data. However, the complex interaction between genes poses challenges in accurately projecting multi-omics data. In this research, we [...] Read more.
By using omics, we can now examine all components of biological systems simultaneously. Deep learning-based drug prediction methods have shown promise by integrating cancer-related multi-omics data. However, the complex interaction between genes poses challenges in accurately projecting multi-omics data. In this research, we present a predictive model for drug response that incorporates diverse types of omics data, comprising genetic mutation, copy number variation, methylation, and gene expression data. This study proposes latent alignment for information mismatch in integration, which is achieved through an attention module capturing interactions among diverse types of omics data. The latent alignment and attention modules significantly improve predictions, outperforming the baseline model, with MSE = 1.1333, F1-score = 0.5342, and AUROC = 0.5776. High accuracy was achieved in predicting drug responses for piplartine and tenovin-6, while the accuracy was comparatively lower for mitomycin-C and obatoclax. The latent alignment module exclusively outperforms the baseline model, enhancing the MSE by 0.2375, the F1-score by 4.84%, and the AUROC by 6.1%. Similarly, the attention module only improves these metrics by 0.1899, 2.88%, and 2.84%, respectively. In the interpretability case study, panobinostat exhibited the most effective predicted response, with a value of −4.895. We provide reliable insights for drug selection in personalized medicine by identifying crucial genetic factors influencing drug response. 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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16 pages, 3885 KiB  
Article
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 1586
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 [...] Read more.
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

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22 pages, 1489 KiB  
Review
AI-Reinforced Wearable Sensors and Intelligent Point-of-Care Tests
by Ghita Yammouri and Abdellatif Ait Lahcen
J. Pers. Med. 2024, 14(11), 1088; https://doi.org/10.3390/jpm14111088 - 1 Nov 2024
Cited by 7 | Viewed by 4977
Abstract
Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing (POCT) and wearable sensors for personalized medicine applications. This review explores the recent advances and the transformative potential of the use of AI in improving wearables and POCT. The integration of AI [...] Read more.
Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing (POCT) and wearable sensors for personalized medicine applications. This review explores the recent advances and the transformative potential of the use of AI in improving wearables and POCT. The integration of AI significantly contributes to empowering these tools and enables continuous monitoring, real-time analysis, and rapid diagnostics, thus enhancing patient outcomes and healthcare efficiency. Wearable sensors powered by AI models offer tremendous opportunities for precise and non-invasive tracking of physiological conditions that are essential for early disease detection and personalized treatments. AI-empowered POCT facilitates rapid, accurate diagnostics, making these medical testing kits accessible and available even in resource-limited settings. This review discusses the key advances in AI applications for data processing, sensor fusion, and multivariate analytics, highlighting case examples that exhibit their impact in different medical scenarios. In addition, the challenges associated with data privacy, regulatory approvals, and technology integrations into the existing healthcare system have been overviewed. The outlook emphasizes the urgent need for continued innovation in AI-driven health technologies to overcome these challenges and to fully achieve the potential of these techniques to revolutionize 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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21 pages, 2042 KiB  
Review
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
Cited by 12 | Viewed by 10745
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 [...] Read more.
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  
Review
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
Cited by 5 | Viewed by 3926
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 [...] Read more.
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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