Back

Global Parkinson's Genetics Program (GP2) Monogenic Network Protocol: Elucidating causative gene variants in hereditary Parkinson's disease

Lange, L. M.; Avenali, M.; Ellis, M.; Illarionova, A.; Keller Sarmiento, I. J.; Tan, A. H.; Madoev, H.; Galandra, C.; Junker, J.; Roopnarain, K.; Solle, J.; Wegel, C.; Fang, Z.-H.; Heutink, P. M.; Kumar, K. R.; Lim, S.-Y.; Valente, E. M.; Nalls, M. A.; Blauwendraat, C.; Singleton, A.; Mencacci, N.; Lohmann, K.; Klein, C.

2022-12-05 genetic and genomic medicine
10.1101/2022.12.01.22282794 medRxiv
Show abstract

The Monogenic Network of the Global Parkinsons Genetics Program (GP2) aims to create an efficient infrastructure to accelerate the identification of novel genetic causes of Parkinsons disease (PD) and to improve our understanding of already identified genetic causes, such as reduced penetrance and variable clinical expressivity of known disease-causing variants. We aim to perform short- and long-read whole-genome sequencing for up to 10,000 patients with parkinsonism.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
npj Parkinson's Disease
○ 105 papers in training set
Top 0.1%
46.4%
Similar papers in this journal
2
Movement Disorders
○ 71 papers in training set
Top 0.1%
22.3%
Similar papers in this journal
50% of probability mass above
3
Annals of Neurology
○ 64 papers in training set
Top 0.3%
5.6%
Similar papers in this journal
4
Neurology Genetics
○ 15 papers in training set
Top 0.1%
2.4%
Similar papers in this journal
5
Brain
◐ 168 papers in training set
Top 1%
2.4%
Similar papers in this journal
6
Genetics in Medicine Open
○ 11 papers in training set
Top 0.1%
1.8%
7
Parkinsonism & Related Disorders
○ 25 papers in training set
Top 0.3%
1.8%
8
eBioMedicine
○ 183 papers in training set
Top 4%
1.4%
9
Genome Medicine
○ 183 papers in training set
Top 3%
1.4%
10
Scientific Data
○ 209 papers in training set
Top 2%
1.1%
11
Genetics in Medicine
○ 78 papers in training set
Top 0.7%
1.1%
12
Annals of Clinical and Translational Neurology
○ 34 papers in training set
Top 0.7%
1.1%
13
Nature Communications
○ 5641 papers in training set
Top 52%
1.1%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.