Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering

Nelke, Christopher; Schroeter, Christina B.; Barman, Sumanta; Stascheit, Frauke; Masanneck, Lars; Theissen, Lukas; Huntemann, Niklas; Walli, Sara; Cengiz, Derya; Dobelmann, Vera; Vogelsang, Anna; Pawlitzki, Marc; Räuber, Saskia; Konen, Felix F.; Skripuletz, Thomas; Hartung, Hans-Peter; König, Simone; Roos, Andreas; Meisel, Andreas; Meuth, Sven G.; Ruck, Tobias

doi:10.1016/j.ebiom.2024.105231

eBioMedicine

2024

DOI: 10.1016/j.ebiom.2024.105231

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Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering

Christopher Nelke,

Christina B. Schroeter,

Sumanta Barman

et al.

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2024

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Cited by 2 publications

References 44 publications

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Towards personalized management of myasthenia gravis phenotypes: From the role of multi-omics to the emerging biomarkers and therapeutic targets

Balistreri,

Vinciguerra,

Magro

et al. 2024

Autoimmunity Reviews

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Towards personalized management of myasthenia gravis phenotypes: From the role of multi-omics to the emerging biomarkers and therapeutic targets

Balistreri,

Vinciguerra,

Magro

et al. 2024

Autoimmunity Reviews

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Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI

Trezza,

Visibelli,

Roncaglia

et al. 2024

Applied Sciences

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Integrating Artificial Intelligence (AI) into Precision Medicine (PM) is redefining healthcare, enabling personalized treatments tailored to individual patients based on their genetic code, environment, and lifestyle. AI’s ability to analyze vast and complex datasets, including genomics and medical records, facilitates the identification of hidden patterns and correlations, which are critical for developing personalized treatment plans. Unsupervised Learning (UL) is particularly valuable in PM as it can analyze unstructured and unlabeled data to uncover novel disease subtypes, biomarkers, and patient stratifications. By revealing patterns that are not explicitly labeled, unsupervised algorithms enable the discovery of new insights into disease mechanisms and patient variability, advancing our understanding of individual responses to treatment. However, the integration of AI into PM presents some challenges, including concerns about data privacy and the rigorous validation of AI models in clinical practice. Despite these challenges, AI holds immense potential to revolutionize PM, offering a more personalized, efficient, and effective approach to healthcare. Collaboration among AI developers and clinicians is essential to fully realize this potential and ensure ethical and reliable implementation in medical practice. This review will explore the latest emerging UL technologies in the biomedical field with a particular focus on PM applications and their impact on human health and well-being.

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Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering

Cited by 2 publications

References 44 publications

Towards personalized management of myasthenia gravis phenotypes: From the role of multi-omics to the emerging biomarkers and therapeutic targets

Towards personalized management of myasthenia gravis phenotypes: From the role of multi-omics to the emerging biomarkers and therapeutic targets

Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI

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