Deep Learning for Genomics: A Concise Overview
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Summary
The strengths of different deep learning models from a genomic perspective are discussed, so as to fit each particular task with a proper deep architecture, and practical considerations of developing modern deep learning architectures for genomics are remarked on.
- Type
- preprint
- Published
- 2018-02-02
- Cited by
- 106
- References
- 262
- Access
- Open access
- OpenAlex
- https://openalex.org/W2785490704
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1572419
Keywords
Deep learning, Genomics, Artificial intelligence, Data science, Computer science
References
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- Imbalanced Learning: Foundations, Algorithms, and Applications
- Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
- The Cancer Genome Atlas Pan-Cancer Analysis Project
- Efficient Estimation of Word Representations in Vector Space
- The identification of cis-regulatory elements: A review from a machine learning perspective
- Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions
- Functional annotation of a full-length mouse cDNA collection
- A deep learning framework for modeling structural features of RNA-binding protein targets
- Auto-Encoding Variational Bayes
- Improving Prediction of Protein Secondary Structure Using Structured Neural Networks and Multiple Sequence Alignments
- Population Structure and Cryptic Relatedness in Genetic Association Studies
- Deciphering the splicing code
- Detection of RNA Polymerase II Promoters and Polyadenylation Sites in Human DNA Sequence
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- Building a Science Gateway For Processing and Modeling Sequencing Data Via Apache Airavata
- Automatic Human-like Mining and Constructing Reliable Genetic Association Database with Deep Reinforcement Learning
- A primer on deep learning in genomics
- Clinical Personal Connectomics Using Hybrid PET/MRI
- Machine-learning-guided directed evolution for protein engineering
- A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate
- Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications
- Viral Genome Deep Classifier
- What If We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks
- Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning
- Dual Adversarial Transfer for Sequence Labeling
- The application of artificial neural networks in metabolomics: a historical perspective
- Robust Bayesian Transfer Learning Between Kalman Filters
- A systematic review of the application of machine learning in the detection and classification of transposable elements
- Deep mixed model for marginal epistasis detection and population stratification correction in genome-wide association studies
- DeepGx: Deep Learning Using Gene Expression for Cancer Classification
- Deep learning for electronic health records: A comparative review of multiple deep neural architectures
- Survey on Techniques, Applications and Security of Machine Learning Interpretability
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