Neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and amyotrophic lateral sclerosis (ALS), pose profound challenges in healthcare due to their progressive neuronal deterioration, which leads to severe cognitive and motor impairments. Despite advances in understanding the molecular and cellular mechanisms of these conditions, early diagnostic tools and effective treatments remain limited. Conventional diagnostic methods frequently fail to detect diseases at their earliest stages, and existing therapies primarily alleviate symptoms without addressing underlying causes.
This Research Topic aims to consolidate cutting-edge research utilizing artificial intelligence (AI), bioinformatics, and in silico approaches for the discovery and validation of biomarkers in neurodegenerative diseases. Emphasizing the improvement of early diagnostic techniques, molecular mechanism insights, and identification of new therapeutic targets, this collection will present interdisciplinary advancements that could redefine precision medicine and support transformative changes in clinical decision-making processes.
We invite contributions across a broad range of computational and biomedical research areas related to neurodegenerative diseases. Key themes of interest include:
• Development and application of AI and machine learning for biomarker detection.
• In silico methodologies for discovering microRNA-based biomarkers.
• Single-cell RNA sequencing technologies to elucidate gene expression patterns at the cellular level.
• Multi-omics data integration to gain a holistic understanding of the molecular mechanisms underlying neurodegeneration.
• Novel bioinformatics tools to process and analyze complex multi-omics data for the understanding of the molecular mechanisms underlying neurodegeneration.
• Precision Medicine Approaches to personalize treatment plans and improve patient outcomes in neurodegenerative diseases.
• Experimental validation of biomarkers identified through AI and In Silico approaches, using in vitro, in vivo.
• Virtual screening approaches for identifying potential drugs and the application of Molecular Dynamics (MD) in exploring protein-ligand interactions relevant to neurogenerative diseases.
Keywords:
AI in Neurodegenerative Diseases, Bioinformatics for Biomarker Discovery, In Silico Modeling, Multi-Omics Integration, Neurodegenerative Biomarkers, Machine Learning for Disease Diagnosis, Single-Cell RNA Sequencing, MicroRNA-Based Biomarkers, Precision M
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and amyotrophic lateral sclerosis (ALS), pose profound challenges in healthcare due to their progressive neuronal deterioration, which leads to severe cognitive and motor impairments. Despite advances in understanding the molecular and cellular mechanisms of these conditions, early diagnostic tools and effective treatments remain limited. Conventional diagnostic methods frequently fail to detect diseases at their earliest stages, and existing therapies primarily alleviate symptoms without addressing underlying causes.
This Research Topic aims to consolidate cutting-edge research utilizing artificial intelligence (AI), bioinformatics, and in silico approaches for the discovery and validation of biomarkers in neurodegenerative diseases. Emphasizing the improvement of early diagnostic techniques, molecular mechanism insights, and identification of new therapeutic targets, this collection will present interdisciplinary advancements that could redefine precision medicine and support transformative changes in clinical decision-making processes.
We invite contributions across a broad range of computational and biomedical research areas related to neurodegenerative diseases. Key themes of interest include:
• Development and application of AI and machine learning for biomarker detection.
• In silico methodologies for discovering microRNA-based biomarkers.
• Single-cell RNA sequencing technologies to elucidate gene expression patterns at the cellular level.
• Multi-omics data integration to gain a holistic understanding of the molecular mechanisms underlying neurodegeneration.
• Novel bioinformatics tools to process and analyze complex multi-omics data for the understanding of the molecular mechanisms underlying neurodegeneration.
• Precision Medicine Approaches to personalize treatment plans and improve patient outcomes in neurodegenerative diseases.
• Experimental validation of biomarkers identified through AI and In Silico approaches, using in vitro, in vivo.
• Virtual screening approaches for identifying potential drugs and the application of Molecular Dynamics (MD) in exploring protein-ligand interactions relevant to neurogenerative diseases.
Keywords:
AI in Neurodegenerative Diseases, Bioinformatics for Biomarker Discovery, In Silico Modeling, Multi-Omics Integration, Neurodegenerative Biomarkers, Machine Learning for Disease Diagnosis, Single-Cell RNA Sequencing, MicroRNA-Based Biomarkers, Precision M
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.