MERGE integrates 9 state-of-the-art machine learning models to predict the pathogenicity of genetic variants. Each model brings unique strengths in analyzing different aspects of genomic data.
DANN (Deleterious Annotation of genetic variants using Neural Networks) uses deep neural networks to predict the functional impact of genetic variants. It combines information from multiple genomic features including conservation scores, functional annotations, and protein-level information.
MetaRNN is a meta-predictor based on recurrent neural networks that integrates predictions from multiple variant effect prediction tools. It leverages sequence context and evolutionary information to provide accurate pathogenicity predictions.
MVP (Missense Variant Pathogenicity) prediction model uses deep residual networks to predict the functional impact of missense variants. It integrates sequence conservation, protein structure, and functional annotations.
gMVP (genome-wide Missense Variant Pathogenicity) extends MVP to genome-wide predictions. It incorporates additional genomic context and regulatory information to improve prediction accuracy across the entire genome.
PrimateAI leverages deep learning on primate genomic data to predict the pathogenicity of missense variants. It uses evolutionary information from six primate species to identify functionally important positions in the human genome.
ESM1b (Evolutionary Scale Modeling) is a large-scale protein language model trained on millions of protein sequences. It captures evolutionary patterns and protein structure information to predict variant effects without explicit structural data.
AlphaMissense is built on AlphaFold's protein structure prediction capabilities. It combines structural insights with evolutionary information to predict the pathogenicity of missense variants with high accuracy across the entire human proteome.
MutFormer uses transformer architecture to model the effects of mutations on protein function. It learns contextualized representations of protein sequences and predicts how mutations affect protein stability and function.
MisFit (Missense Fitness) predicts variant pathogenicity by modeling the fitness effects of mutations. It integrates evolutionary constraints, protein structural information, and functional data to assess the impact of missense variants.