arXiv Machine Learning By Lubna M. Abu Zohair, Marta Vallejo, MD Azher Uddin, John R. Woodward, Hind Zantout

A Machine Learning-Based Framework for Discovering Huntington's Disease Stages: Integrating Graph Representation Learning and clustering to Uncover Progression Dynamics in Longitudinal Enroll-HD Dataset

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arXiv:2606. 06196v1 Announce Type: new Abstract: Huntington's disease (HD) is a progressive brain disorder that gradually affects movement, cognitive function, and behavior.

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arXiv Machine Learning
Jun 8

Explaining Unsupervised Disease Staging in Huntington's Disease: Insights into Model Representations and Clusters

arXiv:2606. 07135v1 Announce Type: new Abstract: Huntington's disease (HD) is a progressive neurodegenerative disorder that affects motor, cognitive, and behavioral functions, where accurate characterization of disease progression remains essential to improve patient outcome and quality of life.

By Lubna Mahmoud Abu Zohair, Hind Zantout
arXiv Computer Vision
Sep 22

DiaSeg: Diagonal Segment Extraction from DTW Paths for Interpretable Gait Analysis

DiaSeg extracts diagonal segments from Dynamic Time Warping (DTW) paths, characterizing each with five geometric features to preserve local alignment information. In a study of 91 subjects across six clinical conditions, these segments revealed consistent unsupervised patterns aligned with biomechanical phases and achieved near-perfect separation of healthy and pathological gait. While cycle‑based methods reached higher overall accuracy, DiaSeg offers phase‑specific interpretability, pinpointing where coordination breaks down within the gait cycle.

By Tresor Y. Koffi, Amel Hidouri, Corentin Legrand, Aur\'elie Bertaux
arXiv Machine Learning
Sep 16

Explainable Graph-theoretical Machine Learning with Application to Alzheimer's Disease Prediction

The paper introduces Explainable Graph-theoretical Machine Learning (XGML) to build individual metabolic brain graphs from FDG-PET data and identify subgraphs predictive of multivariate Alzheimer’s disease outcomes. Using ADNI data, the best model—kernel density estimation with Hellinger distance and random forest—achieved a Pearson correlation of 0.595 across eight cognitive scores, with the highest performance on ADAS13, ADAS11, and ADASQ4. Key edges were found to be jointly but differentially predictive, indicating potential network biomarkers for cognitive decline, though external validation on OASIS3 showed weaker performance likely due to cohort differences.

By Narmina Baghirova, Duy-Thanh V\~u, Duy-Cat Can, Christelle Schneuwly Diaz, Julien Bodlet, Guillaume Blanc, Georgi Hrusanov, Bernard Ries, Oliver Y. Ch\'en