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Deep learning: new computational modelling techniques for genomics
by
Avsec, Žiga
, Gagneur, Julien
, Theis, Fabian J
, Eraslan, Gökcen
in
Analysis
/ Artificial intelligence
/ Base Sequence
/ Cellular signal transduction
/ Computer applications
/ Computer Simulation
/ Deep Learning
/ Gene expression
/ Genetic diversity
/ Genomics
/ Genomics - methods
/ Humans
/ Learning algorithms
/ Machine learning
/ Models, Genetic
/ Neural circuitry
/ Neural Networks, Computer
/ Splicing
/ Supervised Machine Learning
/ Unsupervised Machine Learning
2019
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Deep learning: new computational modelling techniques for genomics
by
Avsec, Žiga
, Gagneur, Julien
, Theis, Fabian J
, Eraslan, Gökcen
in
Analysis
/ Artificial intelligence
/ Base Sequence
/ Cellular signal transduction
/ Computer applications
/ Computer Simulation
/ Deep Learning
/ Gene expression
/ Genetic diversity
/ Genomics
/ Genomics - methods
/ Humans
/ Learning algorithms
/ Machine learning
/ Models, Genetic
/ Neural circuitry
/ Neural Networks, Computer
/ Splicing
/ Supervised Machine Learning
/ Unsupervised Machine Learning
2019
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Do you wish to request the book?
Deep learning: new computational modelling techniques for genomics
by
Avsec, Žiga
, Gagneur, Julien
, Theis, Fabian J
, Eraslan, Gökcen
in
Analysis
/ Artificial intelligence
/ Base Sequence
/ Cellular signal transduction
/ Computer applications
/ Computer Simulation
/ Deep Learning
/ Gene expression
/ Genetic diversity
/ Genomics
/ Genomics - methods
/ Humans
/ Learning algorithms
/ Machine learning
/ Models, Genetic
/ Neural circuitry
/ Neural Networks, Computer
/ Splicing
/ Supervised Machine Learning
/ Unsupervised Machine Learning
2019
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Deep learning: new computational modelling techniques for genomics
Journal Article
Deep learning: new computational modelling techniques for genomics
2019
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Overview
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.
Publisher
Nature Publishing Group
Subject
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