Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
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Summary
This work shows that sequence specificities can be ascertained from experimental data with 'deep learning' techniques, which offer a scalable, flexible and unified computational approach for pattern discovery.
- Type
- article
- Published
- 2015-07-27
- Cited by
- 2,731
- References
- 81
- Access
- Open access
- OpenAlex
- https://openalex.org/W1019830208
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3204652
Keywords
Computational biology, Sequence (biology), Deep learning, Computer science, Artificial intelligence
References
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- Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data
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- Selene: a PyTorch-based deep learning library for sequence data
- A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation
- Deep learning for regulatory genomics
- The identification of cis-regulatory elements: A review from a machine learning perspective
- RNA structure from deep sequencing
- A New Approach for Scalable Analysis of Microbial Communities
- Learning a Hybrid Architecture for Sequence Regression and Annotation
- Deep Learning in Drug Discovery
- Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets
- Deep Learning for Drug-Induced Liver Injury
- Computer vision for high content screening
- Small Genetic Circuits and MicroRNAs: Big Players in Polymerase II Transcriptional Control in Plants
- TensorFlow: Biology's Gateway to Deep Learning?
- Identification of RNA pseudouridine sites using deep learning approaches
- On the Integrity of Deep Learning Oracles
- Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples
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