Deep Learning for Biomedicine
This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to applications of Deep Learning to biomedicine. Feel free to make a pull request to contribute to this list.
Table of contents
Courses
Tutorials & Presentations
- Interactive tutorial to build a convolutional neural network to discover DNA-binding motifs
- Martin Preusse, Gökcen Eraslan: "Deep modeling of DNA sequences with Python & Keras" (PyMunich 2016)
- James Zou: "Deep learning for genomics: Introduction and examples" (Computational Genomics Summer Institute 2017)
- Cory McLean: "Nucleus: TensorFlow toolkit for Genomics" (TensorFlow Dev Summit 2018)
- Lee Cooper: "Predicting Cancer Outcomes from Genomics and Histology with Deep Learning" (NCI Webinars 2018)
- William Noble: "Machine learning methods for making sense of big genomic data" (Computational Genomics Winter Institute 2018)
- Avanti Shrikumar: "Not Just a Black Box: Interpretable Deep Learning for Genomics and Beyond" (NVIDIA GTC 2018)
- Olga Troyanskaya: "The Science of Deep Learning" (National Academy of Sciences Arthur M. Sackler Colloquium 2019)
- Peter Koo: "Interpretable convolutional networks for regulatory genomics" (Models, Inference and Algorithms Meeting 2019)
Projects
Libraries
PyTorch
Selene
- 2019-03 | Selene: a PyTorch-based deep learning library for sequence data | Kathleen M. Chen, Evan M. Cofer, Jian Zhou & Olga G. Troyanskaya | Nature Methods
- http://selene.flatironinstitute.org
- https://github.com/FunctionLab/selene
TensorFlow
Pysster
- 2018-09 | pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks | Stefan Budach, Annalisa Marsico | Bioinformatics
- https://github.com/budach/pysster