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AI and Machine Learning in Molecular Biomarker Discovery and Mechanisms of Neurodegenerative Disorders

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Dr. Seyed Ehsan Saffari

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Guest Editor

Duke-NUS Medical School, Singapore 169857, Singapore
Interests: machine learning; biomarker; neurodegenerative disorder

Special Issue Information

Dear Colleagues,

The transformative potential of machine learning (ML) and artificial intelligence (AI) is reshaping molecular research in neurodegenerative disorders, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), dementia, and mild cognitive impairment (MCI). This Special Issue seeks to highlight ML/AI applications in the analysis of molecular biomarkers, genetic and multi-omics data (genomics, proteomics, metabolomics), and molecular mechanisms underlying neurodegeneration. We invite original research, reviews, and computational studies exploring AI-driven approaches for integrating molecular datasets, identifying novel molecular targets, and elucidating pathways involved in disease onset and progression. Topics include, but are not limited to, machine learning methods for biomarker discovery at the molecular level, prediction of disease risk and progression using molecular data, AI-facilitated classification of molecular subtypes, and integrative omics strategies. Submissions should emphasize translational applications that connect molecular findings to clinical insights, such as improving early diagnosis or identifying potential therapeutic targets. By fostering interdisciplinary collaboration, this issue aims to advance AI/ML-driven molecular discoveries, ultimately enhancing our understanding and treatment of neurodegenerative disorders.

Dr. Seyed Ehsan Saffari
Guest Editor

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Published Papers (2 papers)

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Review

19 pages, 1076 KB

Open AccessReview

Machine Learning-Driven Metabolomic Biomarker Discovery in Glioblastoma: Advances, Challenges, and Future Directions

by Tiffany Shih, Rawad Hodeify, Jasprit Kaur, Mohammad Alnuaimi and Orwa Aboud

Int. J. Mol. Sci. 2026, 27(9), 3842; https://doi.org/10.3390/ijms27093842 - 26 Apr 2026

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Abstract

Glioblastoma (GBM) is an aggressive tumor type known to recur after maximal safe surgical resection followed by concurrent radiation therapy (RT) and chemotherapy (temozolomide—TMZ), and adjuvant TMZ maintenance chemotherapy. It exhibits high intratumor heterogeneity within a single specimen, and thus clinical management remains a challenge due to its rapid progression and high recurrence rate. Machine learning algorithms are currently being implemented in biomarker discovery to develop accurate predictive models that can guide clinical decision making. Emerging evidence identifies metabolomics as a critical player in understanding tumor metabolism and progression. Machine learning computation models have been instrumental in GBM classification and biomarker discovery, as well as the evaluation of tumor staging. Metabolomic profiling of biogenic amines in the setting of surgery, chemoradiation, and understanding relapse also suggests a coordination between metabolic pathways and tumor stage. Many challenges in machine learning and metabolomics-based approaches for disease classification remain due to the dimensionality of datasets, as well as identifying more streamlined panels of metabolite biomarkers. The purpose of this review is to showcase the recent developments in the applications of machine learning in metabolomics as a promising approach to enhancing the biomarker discovery process for future classification and interpretation of patient response to therapies for GBM management in the clinical setting. It also presents the major challenges of implementing machine learning approaches in GBM management and its future directions. Full article

(This article belongs to the Special Issue AI and Machine Learning in Molecular Biomarker Discovery and Mechanisms of Neurodegenerative Disorders)

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35 pages, 3152 KB

Open AccessReview

AI-Resolved Protein Energy Landscapes, Electrodynamics, and Fluidic Microcircuits as a Unified Framework for Predicting Neurodegeneration

by Cosmin Pantu, Alexandru Breazu, Stefan Oprea, Matei Serban, Razvan-Adrian Covache-Busuioc, Octavian Munteanu, Nicolaie Dobrin, Daniel Costea and Lucian Eva

Int. J. Mol. Sci. 2026, 27(2), 676; https://doi.org/10.3390/ijms27020676 - 9 Jan 2026

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Abstract

Research shows that neurodegenerative processes do not develop from a single “broken” biochemistry process; rather, they develop when a complex multi-physics environment gradually loses its ability to stabilize the neuron via a collective action between the protein, ion, field and fluid dynamics of the neuron. The use of new technologies such as quantum-informed molecular simulation (QIMS), dielectric nanoscale mapping, fluid dynamics of the cell, and imaging of perivascular flow are allowing researchers to understand how the collective interactions among proteins, membranes and their electrical properties, along with fluid dynamics within the cell, form a highly interconnected dynamic system. These systems require fine control over the energetic, mechanical and electrical interactions that maintain their coherence. When there is even a small change in the protein conformations, the electric properties of the membrane, or the viscosity of the cell’s interior, it can cause changes in the high dimensional space in which the system operates to lose some of its stabilizing curvature and become prone to instability well before structural pathologies become apparent. AI has allowed researchers to create digital twin models using combined physical data from multiple scales and to predict the trajectory of the neural system toward instability by identifying signs of early deformation. Preliminary studies suggest that deviations in the ergodicity of metabolic–mechanical systems, contraction of dissipative bandwidth, and fragmentation of attractor basins could be indicators of vulnerability. This study will attempt to combine all of the current research into a cohesive view of the role of progressive loss of multi-physics coherence in neurodegenerative disease. Through integration of protein energetics, electrodynamic drift, and hydrodynamic irregularities, as well as predictive modeling utilizing AI, the authors will provide mechanistic insights and discuss potential approaches to early detection, targeted stabilization, and precision-guided interventions based on neurophysics. Full article

(This article belongs to the Special Issue AI and Machine Learning in Molecular Biomarker Discovery and Mechanisms of Neurodegenerative Disorders)

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AI and Machine Learning in Molecular Biomarker Discovery and Mechanisms of Neurodegenerative Disorders

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