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Editor's Choice Series for the Applied Biomedical Data Science Section

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A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Applied Biomedical Data Science".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4654

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Special Issue Editor

Prof. Dr. Jörn Lötsch

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Guest Editor

1. Medical Faculty, Institute of Clinical Pharmacology, Goethe - University, Frankfurt am Main, Germany
2. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
Interests: data science; pain; clinical pharmacology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Editor's Choice Series for the Applied Biomedical Data Science Section presents a curated collection showcasing pioneering methodologies driving the intersection of data science and biomedical research. This Special Issue focuses on innovative approaches, techniques, and tools applied in the realm of data science to unravel complex biological phenomena, facilitate medical discoveries, and improve healthcare outcomes.

Encompassing fields such as big data analytics, machine learning applications, computational modeling, data-driven diagnostics, and bioinformatics, this series offers an extensive exploration of methodologies transforming biomedical data into actionable insights. From predictive modeling for disease prognosis to novel algorithms enhancing drug discovery, these articles will illuminate the impactful role of data science in shaping the future of healthcare.

Submissions for brief reports are not accepted for this Special Issue. Instead, comprehensive studies, methodological analyses, and practical applications at the nexus of data science and biomedical research are encouraged.

Prof. Dr. Jörn Lötsch
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

big data analytics
machine learning applications
computational modeling
data-driven diagnostics
bioinformatics
predictive modeling
disease prognosis
drug discovery
biomedical data analysis
healthcare innovation

Published Papers (6 papers)

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Research

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28 pages, 3449 KiB

Open AccessArticle

Diagnostic Tool for Early Detection of Rheumatic Disorders Using Machine Learning Algorithm and Predictive Models

by Godfrey A. Mills, Dzifa Dey, Mohammed Kassim, Aminu Yiwere and Kenneth Broni

BioMedInformatics 2024, 4(2), 1174-1201; https://doi.org/10.3390/biomedinformatics4020065 - 8 May 2024

Viewed by 230

Abstract

Background: Rheumatic diseases are chronic diseases that affect joints, tendons, ligaments, bones, muscles, and other vital organs. Detection of rheumatic diseases is a complex process that requires careful analysis of heterogeneous content from clinical examinations, patient history, and laboratory investigations. Machine learning techniques have made it possible to integrate such techniques into the complex diagnostic process to identify inherent features that lead to disease formation, development, and progression for remedial measures. Methods: An automated diagnostic tool using a multilayer neural network computational engine is presented to detect rheumatic disorders and the type of underlying disorder for therapeutic strategies. Rheumatic disorders considered are rheumatoid arthritis, osteoarthritis, and systemic lupus erythematosus. The detection system was trained and tested using 70% and 30% respectively of labelled synthetic dataset of 100,000 records containing both single and multiple disorders. Results: The detection system was able to detect and predict underlying disorders with accuracy of 97.48%, sensitivity of 96.80%, and specificity of 97.50%. Conclusion: The good performance suggests that this solution is robust enough and can be implemented for screening patients for intervention measures. This is a much-needed solution in environments with limited specialists, as the solution promotes task-shifting from the specialist level to the primary healthcare physicians. Full article

(This article belongs to the Special Issue Editor's Choice Series for the Applied Biomedical Data Science Section)

10 pages, 424 KiB

Open AccessArticle

Hearables: In-Ear Multimodal Data Fusion for Robust Heart Rate Estimation

by Marek Żyliński, Amir Nassibi, Edoardo Occhipinti, Adil Malik, Matteo Bermond, Harry J. Davies and Danilo P. Mandic

BioMedInformatics 2024, 4(2), 911-920; https://doi.org/10.3390/biomedinformatics4020051 - 1 Apr 2024

Viewed by 584

Abstract

Background: Ambulatory heart rate (HR) monitors that acquire electrocardiogram (ECG) or/and photoplethysmographm (PPG) signals from the torso, wrists, or ears are notably less accurate in tasks associated with high levels of movement compared to clinical measurements. However, a reliable estimation of HR can be obtained through data fusion from different sensors. These methods are especially suitable for multimodal hearable devices, where heart rate can be tracked from different modalities, including electrical ECG, optical PPG, and sounds (heart tones). Combined information from different modalities can compensate for single source limitations. Methods: In this paper, we evaluate the possible application of data fusion methods in hearables. We assess data fusion for heart rate estimation from simultaneous in-ear ECG and in-ear PPG, recorded on ten subjects while performing 5-min sitting and walking tasks. Results: Our findings show that data fusion methods provide a similar level of mean absolute error as the best single-source heart rate estimation but with much lower intra-subject variability, especially during walking activities. Conclusion: We conclude that data fusion methods provide more robust HR estimation than a single cardiovascular signal. These methods can enhance the performance of wearable devices, especially multimodal hearables, in heart rate tracking during physical activity. Full article

(This article belongs to the Special Issue Editor's Choice Series for the Applied Biomedical Data Science Section)

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12 pages, 5186 KiB

Open AccessArticle

Genetic Optimization in Uncovering Biologically Meaningful Gene Biomarkers for Glioblastoma Subtypes

by Petros Paplomatas, Ioanna-Efstathia Douroumi, Panagiotis Vlamos and Aristidis Vrahatis

BioMedInformatics 2024, 4(1), 811-822; https://doi.org/10.3390/biomedinformatics4010045 - 8 Mar 2024

Viewed by 587

Abstract

Background: Glioblastoma multiforme (GBM) is a highly aggressive brain cancer known for its challenging survival rates; it is characterized by distinct subtypes, such as the proneural and mesenchymal states. The development of targeted therapies is critically dependent on a thorough understanding of these subtypes. Advances in single-cell RNA-sequencing (scRNA-seq) have opened new avenues for identifying subtype-specific gene biomarkers, which are essential for innovative treatments. Methods: This study introduces a genetic optimization algorithm designed to select a precise set of genes that clearly differentiate between the proneural and mesenchymal GBM subtypes. By integrating differential gene expression analysis with gene variability assessments, our dual-criterion strategy ensures the selection of genes that are not only differentially expressed between subtypes but also exhibit consistent variability patterns. This approach enhances the biological relevance of identified biomarkers. We applied this algorithm to scRNA-seq data from GBM samples, focusing on the discovery of subtype-specific gene biomarkers. Results: The application of our genetic optimization algorithm to scRNA-seq data successfully identified significant genes that are closely associated with the fundamental characteristics of GBM. These genes show a strong potential to distinguish between the proneural and mesenchymal subtypes, offering insights into the molecular underpinnings of GBM heterogeneity. Conclusions: This study introduces a novel approach for biomarker discovery in GBM that is potentially applicable to other complex diseases. By leveraging scRNA-seq data, our method contributes to the development of targeted therapies, highlighting the importance of precise biomarker identification in personalized medicine. Full article

(This article belongs to the Special Issue Editor's Choice Series for the Applied Biomedical Data Science Section)

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12 pages, 1363 KiB

Open AccessArticle

Classification and Explanation of Iron Deficiency Anemia from Complete Blood Count Data Using Machine Learning

by Siddartha Pullakhandam and Susan McRoy

BioMedInformatics 2024, 4(1), 661-672; https://doi.org/10.3390/biomedinformatics4010036 - 1 Mar 2024

Viewed by 1191

Abstract

Background: Currently, discriminating Iron Deficiency Anemia (IDA) from other anemia requires an expensive test (serum ferritin). Complete Blood Count (CBC) tests are less costly and more widely available. Machine learning models have not yet been applied to discriminating IDA but do well for similar tasks. Methods: We constructed multiple machine learning methods to classify IDA from CBC data using a US NHANES dataset of over 19,000 instances, calculating accuracy, precision, recall, and precision AUC (PR AUC). We validated the results using an unseen dataset from Kenya, using the same model. We calculated ranked feature importance to explain the global behavior of the model. Results: Our model classifies IDA with a PR AUC of 0.87 and recall/sensitivity of 0.98 and 0.89 for the original dataset and an unseen Kenya dataset, respectively. The explanations indicate that low blood level of hemoglobin, higher age, and higher Red Blood Cell distribution width were most critical. We also found that optimization made only minor changes to the explanations and that the features used remained consistent with professional practice. Conclusions: The overall high performance and consistency of the results suggest that the approach would be acceptable to health professionals and would support enhancements to current automated CBC analyzers. Full article

(This article belongs to the Special Issue Editor's Choice Series for the Applied Biomedical Data Science Section)

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17 pages, 1859 KiB

Open AccessArticle

Assessment of Voice Disorders Using Machine Learning and Vocal Analysis of Voice Samples Recorded through Smartphones

by Michele Giuseppe Di Cesare, David Perpetuini, Daniela Cardone and Arcangelo Merla

BioMedInformatics 2024, 4(1), 549-565; https://doi.org/10.3390/biomedinformatics4010031 - 19 Feb 2024

Cited by 1 | Viewed by 973

Abstract

Background: The integration of edge computing into smart healthcare systems requires the development of computationally efficient models and methodologies for monitoring and detecting patients’ healthcare statuses. In this context, mobile devices, such as smartphones, are increasingly employed for the purpose of aiding diagnosis, treatment, and monitoring. Notably, smartphones are widely pervasive and readily accessible to a significant portion of the population. These devices empower individuals to conveniently record and submit voice samples, thereby potentially facilitating the early detection of vocal irregularities or changes. This research focuses on the creation of diverse machine learning frameworks based on vocal samples captured by smartphones to distinguish between pathological and healthy voices. Methods: The investigation leverages the publicly available VOICED dataset, comprising 58 healthy voice samples and 150 samples from voices exhibiting pathological conditions, and machine learning techniques for the classification of healthy and diseased patients through the employment of Mel-frequency cepstral coefficients. Results: Through cross-validated two-class classification, the fine k-nearest neighbor exhibited the highest performance, achieving an accuracy rate of 98.3% in identifying healthy and pathological voices. Conclusions: This study holds promise for enabling smartphones to effectively identify vocal disorders, offering a multitude of advantages for both individuals and healthcare systems, encompassing heightened accessibility, early detection, and continuous monitoring. Full article

(This article belongs to the Special Issue Editor's Choice Series for the Applied Biomedical Data Science Section)

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Review

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12 pages, 255 KiB

Open AccessReview

Exploring the Role of ChatGPT in Oncology: Providing Information and Support for Cancer Patients

by Maurizio Cè, Vittoria Chiarpenello, Alessandra Bubba, Paolo Florent Felisaz, Giancarlo Oliva, Giovanni Irmici and Michaela Cellina

BioMedInformatics 2024, 4(2), 877-888; https://doi.org/10.3390/biomedinformatics4020049 - 25 Mar 2024

Viewed by 644

Abstract

Introduction: Oncological patients face numerous challenges throughout their cancer journey while navigating complex medical information. The advent of AI-based conversational models like ChatGPT (San Francisco, OpenAI) represents an innovation in oncological patient management. Methods: We conducted a comprehensive review of the literature on the use of ChatGPT in providing tailored information and support to patients with various types of cancer, including head and neck, liver, prostate, breast, lung, pancreas, colon, and cervical cancer. Results and Discussion: Our findings indicate that, in most instances, ChatGPT responses were accurate, dependable, and aligned with the expertise of oncology professionals, especially for certain subtypes of cancers like head and neck and prostate cancers. Furthermore, the system demonstrated a remarkable ability to comprehend patients’ emotional responses and offer proactive solutions and advice. Nevertheless, these models have also showed notable limitations and cannot serve as a substitute for the role of a physician under any circumstances. Conclusions: Conversational models like ChatGPT can significantly enhance the overall well-being and empowerment of oncological patients. Both patients and healthcare providers must become well-versed in the advantages and limitations of these emerging technologies. Full article

(This article belongs to the Special Issue Editor's Choice Series for the Applied Biomedical Data Science Section)

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