DeepChrome: deep-learning for predicting gene expression from histone modifications
importance 5/10
ai assisted
Genomics and Chromatin Dynamics epigenetics stage: research
Problem
Predicting gene expression levels from histone modification signals to understand combinatorial effects in gene regulation.
Why it matters
Understanding histone modification effects could help develop epigenetic drugs for diseases like cancer.
What the AI did
DeepChrome, a deep convolutional neural network, classifies gene expression from histone modification data and automatically extracts complex feature interactions, with an optimization-based visualization technique.
Results
not stated in source
Impact
Peer-reviewed method in Bioinformatics with 305 citations, indicating research uptake.
Record
| Venue | Bioinformatics |
|---|---|
| Published | 2016 |
| Work type | article |
| Authors | Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi |
| Citations | 305 (OpenAlex, at import time) |
| References | 45 |
| Model or method | DeepChrome (deep convolutional neural network) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1093/bioinformatics/btw427 |
| Open access | hybrid |
| Keywords | deep learning, histone modifications, gene expression, convolutional neural network, epigenomics |
Importance rating
5
real but narrower advance
Understanding combinatorial histone-modification effects could aid epigenetic drug development for diseases like cancer; abstract states only potential, no scale.