Grigory Khimulya

Publications8
h-index7
Citations699
Highly Influential Citations56
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Unified rational protein engineering with sequence-based deep representation learning
TLDR
Deep learning is applied to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded and broadly applicable to unseen regions of sequence space.
EpiFactors: a comprehensive database of human epigenetic factors and complexes
TLDR
EpiFactors is a manually curated database providing information about epigenetic regulators, their complexes, targets and products, and is practical for a wide range of users, including biologists, pharmacologists and clinicians.
Unified rational protein engineering with sequence-only deep representation learning
TLDR
This work applies deep learning to unlabelled amino acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily, and biophysically grounded.
Low-N protein engineering with data-efficient deep learning
TLDR
A machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution is introduced.
Low-N protein engineering with data-efficient deep learning.
TLDR
A machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution is introduced.
PERFECTOS-APE - Predicting Regulatory Functional Effect of SNPs by Approximate P-value Estimation
Single nucleotide polymorphisms (SNPs) and variants (SNVs) are often found in regulatory regions of human genome. Nucleotide substitutions in promoter and enhancer regions may affect transcription…
Negative selection maintains transcription factor binding motifs in human cancer
TLDR
Analysis of transcription factors with conserved binding motifs can reveal cell regulatory pathways crucial for the survivability of various human cancers and suggest that selection pressure protects cancer cells from rewiring of regulatory circuits.
Unified rational protein engineering with sequence-based deep representation learning
This protocol describes the computational steps necessary to reproduce the results described in the paper "Unified rational protein engineering with sequence-only deep representation learning" by…