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.vscode
.vscode
 
 
MNIST
MNIST
 
 
liberto
liberto
 
 
.DS_Store
.DS_Store
 
 
08022022.ipynb
08022022.ipynb
 
 
1-s2-S0092867421003536-mmc3.csv
1-s2-S0092867421003536-mmc3.csv
 
 
1-s2.0-S0092867421003536-mmc3.xlsx
1-s2.0-S0092867421003536-mmc3.xlsx
 
 
1000 essential genes.ipynb
1000 essential genes.ipynb
 
 
3kb_Promoter.sequences-2.csv
3kb_Promoter.sequences-2.csv
 
 
3kb_Promoter.sequences.csv
3kb_Promoter.sequences.csv
 
 
3kb_Promoter.sequences_ex1.csv
3kb_Promoter.sequences_ex1.csv
 
 
Achilles_common_essentials.csv
Achilles_common_essentials.csv
 
 
BERT - CRISPRoff.ipynb
BERT - CRISPRoff.ipynb
 
 
Cpg Islands.eps
Cpg Islands.eps
 
 
Cpg_Islands.eps
Cpg_Islands.eps
 
 
DeepOff - Positional Methylations.ipynb
DeepOff - Positional Methylations.ipynb
 
 
Essential Genes list violin.eps
Essential Genes list violin.eps
 
 
Essential Genes list.eps
Essential Genes list.eps
 
 
Genome-Wide %GC Strength Zoom.eps
Genome-Wide %GC Strength Zoom.eps
 
 
Genome-Wide %GC Zoom.eps
Genome-Wide %GC Zoom.eps
 
 
Genome-wide %GC.eps
Genome-wide %GC.eps
 
 
Genome-wide %kmer_count.eps
Genome-wide %kmer_count.eps
 
 
Genome-wide Strength vs %GC.eps
Genome-wide Strength vs %GC.eps
 
 
Introduction to NLP.ipynb
Introduction to NLP.ipynb
 
 
Kmer-counts-2.eps
Kmer-counts-2.eps
 
 
Kmer-counts-3.eps
Kmer-counts-3.eps
 
 
Kmer-counts.eps
Kmer-counts.eps
 
 
LICENSE
LICENSE
 
 
Linear-regression_1.eps
Linear-regression_1.eps
 
 
Methylated_Cs and Phenotype Scores.eps
Methylated_Cs and Phenotype Scores.eps
 
 
README.md
README.md
 
 
Random Forest.ipynb
Random Forest.ipynb
 
 
UCSC_TSS.txt
UCSC_TSS.txt
 
 
Violin plot.eps
Violin plot.eps
 
 
Workflow-DeepOff.ipynb
Workflow-DeepOff.ipynb
 
 
achiles-line.eps
achiles-line.eps
 
 
achiles.eps
achiles.eps
 
 
bed_file.csv
bed_file.csv
 
 
computed-heritage.eps
computed-heritage.eps
 
 
dinucleotide_properties.csv
dinucleotide_properties.csv
 
 
gfg_dummy_pic.png
gfg_dummy_pic.png
 
 
good.eps
good.eps
 
 
linear-regression_line.eps
linear-regression_line.eps
 
 
model.ckpt
model.ckpt
 
 
motif-3kb_Promoter.sequences.csv
motif-3kb_Promoter.sequences.csv
 
 
outfile.bed
outfile.bed
 
 
overlap_ATAC.bed
overlap_ATAC.bed
 
 
sequences-03.csv
sequences-03.csv
 
 
small_tree.dot
small_tree.dot
 
 
small_tree.png
small_tree.png
 
 
tempCodeRunnerFile.ipynb
tempCodeRunnerFile.ipynb
 
 
train.txt
train.txt
 
 
tree.dot
tree.dot
 
 
tree.png
tree.png
 
 
trial.py
trial.py
 
 
wgEncodeHaibMethylRrbsAg04449UwSitesRep1.bed
wgEncodeHaibMethylRrbsAg04449UwSitesRep1.bed
 
 
wgEncodeHaibMethylRrbsK562HaibSitesRep1.bed
wgEncodeHaibMethylRrbsK562HaibSitesRep1.bed
 
 
wgEncodeHaibMethylRrbsK562HudsonalphagrowprotSitesRep1.bed9
wgEncodeHaibMethylRrbsK562HudsonalphagrowprotSitesRep1.bed9
 
 
word2vec.wordvectors
word2vec.wordvectors
 
 

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Breaking down Heritable gene silencing with Deep Learning

This repository is still under development.

fork this repo twitter Paper DOI : https://doi.org/10.1016/j.cell.2021.03.025

Pseudo-Abstract

Deep learning algorithms are a growing set of models are trained with raw-data, in this case DNA sequences, and over a cycle of itierations, these can stastically learn to predict an given output data. In this work this model was generated to introduce DNA Promoter sequences and predict the epigenetic hertiable score. The data from this work is from Dr. James Nuñez work.

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Deep Learning to predict phenotype score associated with heritable gene silencing induced by CRISPRoff

doi.org/10.1016/j.cell.2021.03.025

Topics

deep-learning transformers epigenetics crispr dl bert bert-model epigenetic-heritability crispr-dcas9 crisproff

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MIT license
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