81 citations · 106 across the 6 of their papers we have counts for
6 papers
Multi-Block Sparse Functional Principal Components Analysis for Longitudinal Microbiome Multi-Omics Data
Lingjing Jiang, Chris Elrod, Jane J. Kim +3
Microbiome researchers often need to model the temporal dynamics of multiple complex, nonlinear outcome trajectories simultaneously. This motivates our development of multivariate…
BayesTime: Bayesian Functional Principal Components for Sparse Longitudinal Data
Lingjing Jiang, Yuan Zhong, Chris Elrod +3
Modeling non-linear temporal trajectories is of fundamental interest in many application areas, such as in longitudinal microbiome analysis. Many existing methods focus on estimati…
Brain Predictability toolbox: a Python library for neuroimaging based machine learning
Sage Hahn, Dekang Yuan, Wesley Thompson +3
Summary Brain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools designed to work with both tabulated data (in particular brain, psychiatric…
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…
The relative efficiency of time-to-progression and continuous measures of cognition in pre-symptomatic Alzheimer's
Dan Li, Samuel Iddi, Paul S. Aisen +2
Pre-symptomatic (or Preclinical) Alzheimer's Disease is defined by biomarker evidence of fibrillar amyloid beta pathology in the absence of clinical symptoms. Clinical trials in th…
Bayesian latent time joint mixed effect models for multicohort longitudinal data
Dan Li, Samuel Iddi, Wesley K. Thompson +1
Characterization of long-term disease dynamics, from disease-free to end-stage, is integral to understanding the course of neurodegenerative diseases such as Parkinson's and Alzhei…