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Table 1 Summary of trained modules and models

From: MMSplice: modular modeling improves the predictions of genetic variant effects on splicing

MMSplice model Training data Architecture Loss function Target value Parameters
Donor module GENCODE 24, positive: annotated donors, negative: random sequence (“Methods” section) Four layer neural network with dropout and batch normalization, Additional file 1: Figure S1A Binary cross entropy Positive vs. negative 18,049
Acceptor module GENCODE 24, positive: annotated acceptors, negative: random sequence (“Methods” section) Two layer conv. neural network with dropout and batch normalization, Additional file 1: Figure S1B Binary cross entropy Positive vs. negative 4833
Exon 5 module MPRA [18] exonic sequence One conv. layer shared with the Exon 3 module, followed with one specific dense layer, Additional file 1: Figure S2 Binary cross entropy Ψ 5 6145
Exon 3 module MPRA [18] exonic sequence One conv. layer shared with the Exon 5 module, followed with one specific dense layer, Additional file 1: Figure S2 Binary cross entropy Ψ 3 6145
Intron 5 module MPRA [18] intronic sequence One conv. layer shared with the Intron 3 module, followed with one specific dense layer, Additional file 1: Figure S2 Binary cross entropy Ψ 3 13,825
Intron 3 module MPRA [18] intronic sequence One conv. layer shared with the Intron 5 module, followed with one specific dense layer, Additional file 1: Figure S2 Binary cross entropy Ψ 5 13,825
Δlogit(Ψ) model Vex-seq [29] Linear regression Huber loss Δlogit(Ψ), Eq. 2 9
Splicing efficiency model (in vivo) MaPSy (“Methods” section) Linear regression Huber loss Splicing efficiency, Eq. 10 5
Splicing efficiency model (in vitro) MaPSy (“Methods” section) Linear regression Huber loss Splicing efficiency, Eq. 10 5
Pathogenicity model (w/o phyloP and CADD) ClinVar [30] [ − 10, 10] around donor, [ − 40, 10] around acceptor Logistic regression Binary cross entropy Pathogenic vs. benign 14
Pathogenicity model (with phyloP and CADD) ClinVar [30] [ − 10, 10] around donor, [ − 40, 10] around acceptor Logistic regression Binary cross entropy Pathogenic vs. benign 18