This is a collection of papers on "deep learning" related to computational biology. The point of this repository is to get papers that others think are important or translate well to problems in computational biology. I hope to pick a subset of these for our "deep learning for comp bio" reading group in Spring 2016.
In particular, my interests lie in:
- dimension reduction techniques
- clustering techniques
- functional annotation
- other things that I probably don't know about that are cool
- A Probabilistic Theory of Deep Learning
- Deep learning
- Representation Learning: A Review and New Perspectives
- ADAGE analysis of publicly available gene expression data collections illuminates Pseudomonas aeruginosa-host interactions
- Basset: Learning the regulatory code of the accessible genome with deep convolutional neural networks.
- Deep learning of the tissue-regulated splicing code
- Predicting effects of noncoding variants with deep learning–based sequence model
- The human splicing code reveals new insights into the genetic determinants of disease
- Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders
- A Statistical View of Deep Learning
- Autoencoders, Unsupervised Learning, and Deep Architectures
- Deep Convolutional Networks on Graph-Structured Data
- Deep rectifier sparse neural networks
- Deep Learning with Nonparametric Clustering
- Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
- k-Sparse Autoencoders
- Rectified Linear Units Improve Restricted Boltzmann Machines
- Stochastic Pooling for Regularization of Deep Convolutional Neural Networks
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent