List of deep learning implementations in biology
[Note: this list now lives at GitHub, where it will be continuously updated, so please go there instead!]
I’m going to start collecting papers on, and implementations of, deep learning in biology (mostly genomics, but other areas as well) on this page. It’s starting to get hard to keep up! For the purposes of this list, I’ll consider things like single-layer autoencoders, although not literally “deep”, to qualify for inclusion. The categorizations will by necessity be arbitrary and might be changed around from time to time.
Please let me know about the stuff I missed!
A convolutional net that can learn features which are useful for predicting properties of novel molecules; “molecular fingerprints”. The net works on a graph where atoms are nodes and bonds are edges. Developed by the group of Ryan Adams, who co-hosts the very good Talking Machines podcast.
Pcons2 – Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns [web interface]
Here, a “deep random forest” with five layers is used to improve predictions of which residues (amino acids) in a protein are physically interacting which each other. This is useful for predicting the overall structure of the protein (a very hard problem.)
In modeling gene expression, the inputs are typically numerical values (integers or floats) estimating how much RNA is produced from a DNA template in a particular cell type or condition.
This is a Theano implementation of stacked denoising autoencoders for extracting relevant patterns from large sets of gene expression data, a kind of feature construction approach if you will. I have played around with this package quite a bit myself. The authors initially published a conference paper applying the model to a compendium of breast cancer (microarray) gene expression data, and more recently posted a paper on bioRxiv where they apply it to all available expression data (microarray and RNA-seq) on the pathogen Pseudomonas aeruginosa. (I understand that this manuscript will soon be published in a journal.)
Learning structure in gene expression data using deep architectures [paper]
This is also about using stacked denoising autoencoders for gene expression data, but there is no available implementation (as far as I could tell). Included here for the sake of completeness (or something.)
This deals with a specific prediction task, namely to predict the expression of specified target genes from a panel of about 1,000 pre-selected “landmark genes”. As the authors explain, gene expression levels are often highly correlated and it may be a cost-effective strategy in some cases to use such panels and then computationally infer the expression of other genes. Based on Pylearn2/Theano.
Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model [paper]
The authors use stacked autoencoders to learn biological features in yeast from thousands of microarrays. They analyze the hidden layer representations and show that these encode biological information in a hierarchical way, so that for instance transcription factors are represented in the first hidden layer.
Predicting enhancers and regulatory regions
Here the inputs are typically “raw” DNA sequence, and convolutional networks (or layers) are often used to learn regularities within the sequence. Hat tip to Melissa Gymrek (http://melissagymrek.com/science/2015/12/01/unlocking-noncoding-variation.html) for pointing out some of these.
Made for predicting the function of non-protein coding DNA sequence. Uses a convolution layer to capture regulatory motifs (i e single DNA snippets that control the expression of genes, for instance), and a recurrent layer (of the LSTM type) to try to discover a “grammar” for how these single motifs work together. Based on Keras/Theano.
Based on Torch, this package focuses on predicting the accessibility (or “openness”) of the chromatin – the physical packaging of the genetic information (DNA+associated proteins). This can exist in more condensed or relaxed states in different cell types, which is partly influenced by the DNA sequence (not completely, because then it would not differ from cell to cell.)
Like the packages above, this one also models chromatin accessibility as well as the binding of certain proteins (transcription factors) to DNA and the presence of so-called histone marks that are associated with changes in accessibility. This piece of software seems to focus a bit more explicitly than the others on predicting how single-nucleotide mutations affect the chromatin structure. Published in a high-profile journal (Nature Methods).
This is from the group of Brendan Frey in Toronto, and the authors are also involved in the company Deep Genomics. DeepBind focuses on predicting the binding specificities of DNA-binding or RNA-binding proteins, based on experiments such as ChIP-seq, ChIP-chip, RIP-seq, protein-binding microarrays, and HT-SELEX. Published in a high-profile journal (Nature Biotechnology.)
This package is for predicting enhancers (stretches of DNA that can enhance the expression of a gene under certain conditions or in a certain kind of cell, often working at a distance from the gene itself) based on heterogeneous data from (e.g.) the ENCODE project, using 1,114 features altogether.
DEEP: a general computational framework for predicting enhancers
Genome-Wide Prediction of cis-Regulatory Regions Using Supervised Deep Learning Methods (and several other papers applying various kinds of deep networks to regulatory region prediction) [code][one paper out of several]
Wyeth Wasserman’s group have made a kind of toolkit (based on the Theano tutorials) for applying different kinds of deep learning architectures to cis-regulatory element (DNA stretches that can modulate the expression of a nearby gene) prediction. They use a specific “feature selection layer” in their nets to restrict the number of features in the models. This is implemented as an additional sparse one-to-one linear layer between the input layer and the first hidden layer of a multi-layer perceptron.
This implementation uses a stacked autoencoder with a supervised layer on top of it to predict whether a certain type of genomic region called “CpG islands” (stretches with an overrepresentation of a sequence pattern where a C nucleotide is followed by a G) is methylated (a chemical modification to DNA that can modify its function, for instance methylation in the vicinity of a gene is often but not always related to the down-regulation or silencing of that gene.) This paper uses a network structure where the hidden layers in the autoencoder part have a much larger number of nodes than the input layer, so it would have been nice to read the authors’ thoughts on what the hidden layers represent.
This is a convolutional network (Lasagne/Theano) based approach for “” It is interesting because most neural network approaches for high-dimensional molecular measurements (such as those in the gene expression category above) have used autoencoders rather than convolutional nets.
Deep learning for population genetic inference [paper]
No implementation available yet but says an open-source one will be made available soon.
This is a harder category to populate because a lot of theoretical work on neural networks and deep learning has been intertwined with neuroscience. For example, recurrent neural networks have long been used for modeling e.g. working memory and attention. In this post I am really looking for pure applications of DL rather than theoretical work, although that is extremely interesting.
For more applied DL, I have found
Deep learning for neuroimaging: a validation study [paper]
SPINDLE: SPINtronic deep learning engine for large-scale neuromorphic computing [paper]
I’m sure there are many others. Maybe digging up some seminal neuroscience papers modeling brain areas and functions with different kinds of neural networks would be a worthy topic for a future blog post.