Genomic Pre-trained Network
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Updated
Aug 11, 2024 - Jupyter Notebook
Genomic Pre-trained Network
Tissue-specific variant effect predictions on splicing
Fully convolutional deep learning variant effect predictor architecture
Predicting the effect of mutations on protein stability and protein binding affinity using pretrained neural networks and a ranking objective function.
Clinical Whole Genome and Exome Sequencing Pipeline
We present Envision, an accurate predictor of protein variant molecular effect, trained using large-scale experimental mutagenesis data. All data and software in this study are freely available. The training data set and all code used to train the models and generate the figures presented in this manuscript are available here. Envision predictio…
CADD-SV – a framework to score the effect of structural variants
Implementation of SpliceAI, Illumina's deep neural network to predict variant effects on splicing, in PyTorch.
Pipeline for variant annotation using Variant Effect Predictor (VEP)
Implementation of evolutionary model of variant effect (EVE), a deep generative model of evolutionary data, in PyTorch.
A Haskell script that performs basic parsing on the default output of ensembl-vep and variant-calling format (vcf) files.
Preliminary analysis of a combined DMS dataset, including clustering and VEP benchmark
Deep learning model for non-coding regulatory variants
Deep learning framework to predict functional effects of missense variants in human
interactive variant tables for easy filtering
Master's Thesis Project
An explorative tool to identify functional hotspots
Transcriptional variant verification to validate predicted variants from genomic data in expressed transcripts
Experimenting with protein language model predictions
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