TrendBio

#1 · ML / AI methods

Joint inference of disease time, progression subtypes, and multimodal trajectories

4 preprints · ↑ 2.3× vs 4-wk avg
Items per day, last 8 weekstoday: 4

The dominant thread is subject-level modeling of change over time rather than cross-sectional comparison. A connectome-constrained dynamic model jointly infers each subject's disease time and data-driven progression subtypes from longitudinal Parkinson's morphometry in PPMI, while TractSpLearn applies a shared-manifold framework to DKI tract features for individualized detection of subtle white matter alterations in mild traumatic brain injury. What is new in the first is the joint estimation of time and subtype within one scalable model. The remaining items address adjacent methodological concerns: pyAvalanches standardizes spatiotemporal propagation analysis of EEG neuronal avalanches, and AnnoAudit shows that a permissive marker rule can manufacture a spurious neuronal population in a TBI single-cell atlas. These items share a methodological rather than biological endpoint.

Preprints

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