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Machine Learning Approaches for Early Diagnosis of Alzheimer’s Disease

Edited by:
Jason Moore: Cedars-Sinai Medical Center, USA

Submission Status: Open   |   Submission Deadline: 15 February 2024

BioData Mining is calling for submissions to our Collection on "Machine Learning Approaches for Early Diagnosis of Alzheimer’s Disease".

Image credit: ThitareeSarmkasat / Getty Images / iStock

About the collection

Alzheimer’s disease is a neurodegenerative disease leading to memory loss and dementia. Managing this common disease requires early diagnosis and effective treatment strategies. However, Alzheimer’s disease has proven to be extremely complex with diverse etiological factors and extensive biological and clinical heterogeneity. Big biomedical data collected from cases and health controls hold the promise to better diagnosis and treatment, but require powerful computational and statistical methods for addressing disease complexity. This Collection will feature peer-reviewed papers focusing on the development, evaluation, and application of new artificial intelligence methods and software for the analysis of Alzheimer’s disease data. Topics will include:

• Machine learning methods for early Alzheimer’s prediction and diagnosis
• Feature selection methods for reducing data dimensionality
• Methods for interpreting machine learning models of Alzheimer’s
• Methods for improving the fairness of machine learning models
• Automated machine learning methods for expanding access of tools and software
• Large language models for the analysis of text from research or clinical studies of Alzheimer’s
• Deep learning neural network methods for the analysis of brain images from Alzheimer’s patients
• Evolutionary and nature-inspired algorithms for the analysis of Alzheimer’s disease data
• Algorithms and databases for Alzheimer’s knowledge engineering to inform machine learning models
• Evaluation and deployment of machine learning models for the clinical care of Alzheimer’s patients

There are currently no articles in this collection.

Submission Guidelines

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Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Machine Learning Approaches for Early Diagnosis of Alzheimer’s Disease" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.