Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs
RT Gerraty, A Provost, L Li, E Wagner… - Frontiers in Aging …, 2023 - frontiersin.org
The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's
worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk …
worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk …
Transmembrane protein 175, a lysosomal ion channel related to Parkinson's disease
T Tang, B Jian, Z Liu - Biomolecules, 2023 - mdpi.com
Lysosomes are membrane-bound organelles with an acidic lumen and are traditionally
characterized as a recycling center in cells. Lysosomal ion channels are integral membrane …
characterized as a recycling center in cells. Lysosomal ion channels are integral membrane …
Genomic machine learning meta-regression: insights on associations of study features with reported model performance
EJ Barnett, DG Onete, A Salekin… - IEEE/ACM Transactions …, 2023 - ieeexplore.ieee.org
Many studies have been conducted with the goal of correctly predicting diagnostic status of
a disorder using the combination of genomic data and machine learning. It is often hard to …
a disorder using the combination of genomic data and machine learning. It is often hard to …
Transfer learning with false negative control improves polygenic risk prediction
Polygenic risk score (PRS) is a quantity that aggregates the effects of variants across the
genome and estimates an individual's genetic predisposition for a given trait. PRS analysis …
genome and estimates an individual's genetic predisposition for a given trait. PRS analysis …
Genetics in parkinson's disease: From better disease understanding to machine learning based precision medicine
M Aborageh, P Krawitz, H Fröhlich - Frontiers in Molecular Medicine, 2022 - frontiersin.org
Parkinson's Disease (PD) is a neurodegenerative disorder with highly heterogeneous
phenotypes. Accordingly, it has been challenging to robustly identify genetic factors …
phenotypes. Accordingly, it has been challenging to robustly identify genetic factors …
Context Matters: Using Genomic Knowledge to Improve Disorder Classification Models
EJ Barnett - 2023 - soar.suny.edu
Despite heritability estimates that suggest a high ceiling for the classification of many
complex genetic disorders, current models have only been moderately successful at …
complex genetic disorders, current models have only been moderately successful at …
[PDF][PDF] On the Use of Machine Learning Methods for Genomic Prediction
CM Kelly - 2023 - tara.tcd.ie
This thesis is concerned with exploring the use of machine learning in comparison to
traditional linear methods for the genomic prediction of complex traits. Special attention is …
traditional linear methods for the genomic prediction of complex traits. Special attention is …