This is a list of implementations of deep learning methods to biology, originally published on Follow the Data. There is a slant towards genomics because that's the subfield that I follow most closely.
Please, contribute to this growing list, especially in categories that I haven't covered well!
You might also want to refer to the awesome deepbio list.
- Reviews
- Model repositories and resources
- Sequence modelling
- Multi-omics integration
- Protein biology
- Genomics
- Chemoinformatics and drug discovery
- Biomarker discovery
- Metabolomics
- Generative models
- Population genetics
- Systems biology
These are not implementations as such, but contain useful pointers. Because review papers in this field are more time-sensitive, I have added the month of journal publication. Note that the original preprint may in some cases have been available online long before the published version.
(2021-11) A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Synergy, and Drug-Drug Interaction Prediction [open access paper]
In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks. We provide fundamental definitions, compare existing model architectures and discuss performance metrics, datasets and evaluation protocols. In addition, we emphasize possible high impact applications and important future research directions in this domain.
(2019-12) Deep learning of pharmacogenomics resources: moving towards precision oncology [Briefings in Bioinformatics]
(2019-04) Deep learning: new computational modelling techniques for genomics [Nature Reviews Genetics paper]
This is a very nice conceptual review of how deep learning can be used in genomics. It explains how convolutional networks, recurrent networks, graph convolutional networks, autoencoders and GANs work. It also explains useful concepts like multi-modal learning, transfer learning, and model explainability.
(2019-01) A guide to deep learning in healthcare [Nature Medicine paper]
From the abstract: "Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed."
(2018-11) A primer on deep learning in genomics [Nature Genetics paper][Colaboratory notebook with tutorial]
This review, which features yours truly as one of its co-authors, is billed as a 'primer' which means it tries to help genomics researchers get started with deep learning. We tried to accomplish this by highlighting many practical issues such as tooling (not only deep learning libraries but also GPU cloud platforms, model zoos and online courses), defining your deep learning problem, explainability and troubleshooting. We also made a tutorial on Colaboratory that shows how to set up and run a simple convolutional network model for learning binding motifs, and how to inspect the model's predictions after it has been trained.
(2018-10) Deep learning in biomedicine [Nature Biotechnology paper]
From the abstract: "Deep learning is beginning to impact biological research and biomedical applications as a result of its ability to integrate vast datasets, learn arbitrarily complex relationships and incorporate existing knowledge. Already, deep learning models can predict, with varying degrees of success, how genetic variation alters cellular processes involved in pathogenesis, which small molecules will modulate the activity of therapeutically relevant proteins, and whether radiographic images are indicative of disease. However, the flexibility of deep learning creates new challenges in guaranteeing the performance of deployed systems and in establishing trust with stakeholders, clinicians and regulators, who require a rationale for decision making. We argue that these challenges will be overcome using the same flexibility that created them; for example, by training deep models so that they can output a rationale for their predictions. Significant research in this direction will be needed to realize the full potential of deep learning in biomedicine."
(2018-04) Opportunities And Obstacles For Deep Learning In Biology And Medicine [bioRxiv preprint][J Roy Soc interface paper]
This impressive collaborative review was written completely in the open on Github. It is focused on discussing how deep learning may be able to transform patient classification and treatment as well as fundamental biological research in the future, and what the main obstacles are that could prevent it from happening. A lot of interesting points are brought up here. Together with the review listed below, which has a more technical slant, you will get a good overview of how deep learning is used and can be used in biology and medicine.
(2017-01) Deep learning for health informatics [open access paper]
An overview of several types of deep nets and their applications in translational bioinformatics, medical imaging, "pervasive sensing", medical data and public health.
(2016-07) Deep learning for computational biology [open access paper]
This is a very nice review of deep learning applications in biology. It primarily deals with convolutional networks and explains well why and how they are used for sequence (and image) classification.
The Kipoi repository accelerates community exchange and reuse of predictive models for genomics [Github][Website][Paper]
Kipoi is a model zoo for genomics, installable by a simple pip install, which provides a consistent interface to hundreds of predictive models in genomics. Kipoi implements a standard set of data loaders for training and prediction of sequence models in deep learning.
DragoNN provides a toolkit for learning about modelling regulatory sequence with neural networks. It has tools for interpreting sequence models and web-based tutorials using Jupyter Notebooks for teaching interactive model manipulation and visualization.
This is a collection of mostly NLP inspired models for modelling biological sequences, such as proteins or genes. Perhaps these models should be moved to other sections as language models in biology become more mainstream.
Continuous Distributed Representation of Biological Sequences for Deep Genomics and Deep Proteomics[github][paper]
The GitHub summary reads: "We introduce a new representation for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. Biovectors are basically n-gram character skip-gram wordvectors for biological sequences (DNA, RNA, and Protein). In this work, we have explored biophysical and biochemical meaning of this space. In addition, in variety of bioinformatics tasks we have shown the strength of such a sequence representation."
pysster: Learning Sequence and Structure Motifs in DNA and RNA Sequences using Convolutional Neural Networks[github][preprint]
A toolbox for learning motifs from DNA/RNA sequence data using convolutional neural networks, this Tensorflow-based library supposedly runs on GPU out of the box and also does things like hyperparameter optimization and visualizations of what different network layers are learning.
Unified rational protein engineering with sequence-based deep representation learning [github][paper]
The authors introduce UniRep, an early language model for protein sequences based on mLSTMs (multiplicative LSTMs). It's trained on 24 million protein sequences from UniRef50 and can be used to convert protein sequences into numerical vector representations that contain information about protein properties. For example, the representations can be used to train downstream predictors of protein stability and function. UniRep can also be used as a "babbler", or generative model, to design new proteins.
Natural language predicts viral escape [github][paper]
This paper attempts to model how viruses evade being detected by the immune system ("viral escape") by using a language model on amino acids implemented with a BiLSTM-based networks. They posit that a sequence that enables escape from the immune system should have high viability, which they liken to the grammaticality of a sentence, while also having different "semantics", i.e. looking different from an antigenic point of view. The grammaticality is learned in the final layer as a prediction task, whereas the semantics are extracted from the representation in the next to last layer.
Genomic-ULMFiT: ULMFiT for Genomic Sequence Data [github]
This repo is an implementation of FastAI's ULMFiT language transfer learning model for genomics. ULMFiT is based on an AWD-LSTM model and has been shown to be very effective for solving various text classification tasks. Here, the repo's author has extended FastAI's classes with specific subclasses for DNA sequence data. The concept with ULMFiT is that you (1) learn a language model from a large body of text in an unsupervised way (ie you don't need any labels) by having the model guess the next word (or token); (2) take the language model from step (1) and fine-tune it on the (probably) smaller labeled data set that you want to do classification on, but still do the training without labels in this step (and try to predict the next word), (3) finally fine-tune on the final classification task, using the labels. In genomics, the large body of text in step (1) could be, for instance, the whole human genome, or some other subset of GenBank/Sequence Read Archive/... The author shows that this approach works quite well for a range of classification problems, like E. coli and human promoter classification, metagenomic classification, enhancer classification and mRNA/lincRNA classification.
Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences [github][preprint]
In this work from Facebook's (now Meta's) AI group, the BERT language model is used to train a language model, ESM-1, on 86 billion amino acids across 250 million sequences. Like with ULMFiT (above), the idea is to use transfer learning: pre-training on a massive amount of data to teach a model something about the underlying logic of the language of DNA or proteins, in order to then be able to fine-tune the model for specific tasks.
ProGen2: Exploring the Boundaries of Protein Language Models [github][preprint]
Surprisingly, Salesforce has also been involved in protein language model research for quite some time. In this preprint they introduce a suite of protein language models that can be used for things like sequence fitness prediction and sequence generation.
MSA Transformer [github][preprint]
Here, the same team from Meta that introduced the ESM-1 model (above) show that a different type of transformer, which uses multiple sequence alignments (MSA) as input instead of protein sequences, can achieve even better results than a BERT-style transformer while using a smaller number of parameters. They introduce different forms of row and column attention to extract as much information from the MSAs as possible. The GitHub repo contains one trained version of the model, ESM-MSA-1b.
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing [github][huggingface][preprint]
A large-scale effort to train and benchmark Transformer models on protein sequences, this project even has provided several of its models to the public on the HuggingFace model hub. The abstract starts: "Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112-times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024)."
Effective gene expression prediction from sequence by integrating long-range interactions [github][tensorflow hub][paper]
Can a transformer architecture help solve the hard problem of relating genomic enhancers to gene expression? It is experimentally laborious to connect distal enhancers to genes, and the presence of many long-range interactions has made it challenging to learn them from data via correlations (due to multiple testing), convolutional networks (too short receptive fields) or recurrent networks (hard to keep a long enough memory.) Now researchers at Deep Mind, Calico and Google have introduced the "enhancer transformer", ie Enformer, which can leverage the self-attention mechanism to learn enhancer/gene expression interactions with a much longer range than before. Commendably, the authors have not only published the code on github but there is also a pretrained model on Tensorflow Hub.
A BERT-like masked language model trained on human DNA sequence using k-mers as tokens.
GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics [github][paper]
Like the earlier Enformer, this is a transformer model for nucleotides (DNA or RNA), but with different design and goals. Whereas Enformer is a pre-trained model for mammalian genomes (human and mouse), GenSLM is intended as a foundation model for less complex genomes, such as bacteria and viruses. It is pre-trained on 110 million prokaryotic (bacterial and archaeal) genomes using a GPT-style ("predict the next token") loss. The tokens are codons (nucleotide triplets), and consequently, the trained model can be "prompted" in GPT-3 fashion with codons. The foundation model can be further finetuned on a subset of genomes ("evolutionary finetuning"), in the case of this paper 1.5 million sequences SARS-CoV-2 genomes, yielding a SARS-CoV-2 specific language model, which contains implicit knowledge of the virus' evolutionary landscape and can be used to identify variants of concern. A further interesting twist in this paper is that long-range interactions, which Enformer tried to solve with convolutions coupled with self-attention, are modelled using diffusion models (á la Stable Diffusion.)
The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics [github][preprint] This is work from Instadeep, an ML consultancy that was acquired by BioNTech, the developer of the Pfizer mRNA vaccine against SARS-CoV-2, in Januari 2023. The authors have attempted to build foundation models for nucleotide (i.e., DNA and RNA) sequences based on a broader variety of genomes than in previous efforts, namely >3000 human genomes and 850 non-human genomes from different organisms. These models are meant to be used for transfer learning and are claimed to work well for downstream prediction tasks even in low-data settings. Concretely, the models are BERT-like models with nucleotide 6-mers as tokens.
GENA-LM [github] This is a masked language model trained on human DNA sequence (like e.g. the DNABERT or Nucleotide Transformer above.) According to the github page, one difference from DNABERT is that GENA-LM uses BPE tokenization instead of k-mers and can thus handle an input sequence size of about 3000 nucleotides (512 BPE tokens) compared to 510 nucleotides of DNABERT.
Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine. [paper]
A review paper about the potential of deep learning for multi-omics data integration.
This category is divided into sub-categories.
Highly accurate protein structure prediction with AlphaFold [github][paper]
This one probably needs no introduction. DeepMind released the first version of its protein-folding method AlphaFold in 2018, when it won the prestigious CASP competition. A completely redesigned version, described in this paper (and sometimes called AlphaFold2) won the same competition in 2020 with a very wide margin. The new version used a component called the "Evoformer", a kind of transformer which iteratively processed a set of aligned protein sequences and a matrix of pairwise interaction between amino acids to generate a representation that can be used as input to a folding module, which uses a specific type of attention called "Invariant pointwise attention". The original AlphaFold paper has been followed by many papers that show how new tasks can be solved by modifying the model in different ways.
OpenFold [github]
This is a Pytorch-based, open-source reimplementation of AlphaFold, which reproduces practically all of the functionality. Before AlphaFold made its model weights generally available, OpenFold was a way to train your own folding model.
MiniFold: a re-implementation of DeepMind's AlphaFold [github]
One of the more spectacular successes of deep learning in biology in the recent years was when DeepMind's AlphaFold model won the CASP13 protein structure prediction challenge. It was originally not listed on this page because there was no open implementation, but this has since changed. In any case, MiniFold was an attempt to re-implement AlphaFold in a somewhat more minimalistic way.
Evolutionary-scale prediction of atomic level protein structure with a language model [github][preprint]
The Meta research group that created ESM-1, ESM-MSA and other models described elsewhere in this document here show that large language models can also be used to do protein structure prediction. Given enough parameters and training data, the trained model starts to implicitly learn information about 3D conformation. The authors claim that this LLM-only approach (i.e. it does not use multiple sequence alignments or backbone inputs) is up to 60x faster than other approaches, such as AlphaFold. They use the model to make structure predictions for 600 million metagenomic (environmental DNA) samples.
Protein Loop Modeling Using Deep Generative Adversarial Network[paper][website]
From the abstract: "Biology and medicine have a long-standing interest in computational structure prediction and modeling of proteins. There are often missing regions or regions that need to be remodeled in protein structures. The process of predicting particular missing regions in a protein structure is called loop modeling. In this paper, we propose a generative adversarial network (GAN) in deep learning for loop modeling using the idea of image inpainting. The generative network is to capture the context of the loop region and predict the missing area. The adversarial network is to make the prediction look real and provide gradients to the generative network. The proposed network was evaluated on a common benchmark for loop modeling. Experiments show that our method can successfully predict the loop region and has achieved better performance than the state-of-the-art tools. To our knowledge, this work represents the first attempt of using GAN for any bioinformatics studies."
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.)
Low-N protein engineering with data-efficient deep learning [preprint]
Based on the UniRep model (described elsewhere in this document), the authors introduce a machine learning paradigm or workflow for training models predicting protein properties and designing novel sequence variants based on a very small number of labelled samples (as few as 20-30). In this paradigm, a base model is trained in an unsupervised manner on a large set of diverse protein sequences (like in the original UniRep paper), and then this model is trained further with the same loss function but on a more restricted family of proteins which is evolutionarily related to the target protein. This procedure is called "evotuning" or evolutionary finetuning. After this step, the authors show that supervised learning using the representation created by the evotuned model often works well given only a small number of labelled samples. With the supervised model in hand, in silico directed evolution can be used to design a new variant of the target protein with desired characteristics.
ProteinGAN: Expanding functional protein sequence space using generative adversarial networks [code][preprint]
From the abstract: "De novo protein design for catalysis of any desired chemical reaction is a long standing goal in protein engineering, due to the broad spectrum of technological, scientific and medical applications. Currently, mapping protein sequence to protein function is, however, neither computationionally nor experimentally tangible. Here we developed ProteinGAN, a specialised variant of the generative adversarial network that is able to 'learn' natural protein sequence diversity and enables the generation of functional protein sequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Using malate dehydrogenase as a template enzyme, we show that 24% of the ProteinGAN-generated and experimentally tested sequences are soluble and display wild-type level catalytic activity in the tested conditions in vitro, even in highly mutated (>100 mutations) sequences. ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diverse novel functional proteins within the allowed biological constraints of the sequence space."
Robust deep learning based protein sequence design using ProteinMPNN [code][preprint]
This work presents a method for designing a protein sequence that is predicted to fold into a specified conformation, i.e. in a way the reverse of AlphaFold: going from structure to sequence. This is achieved by using a type of graph neural network, a message passing neural network (MPNN.) The diversity of the generated sequences can be tuned, and the authors test the performance of the method both using AlphaFold and experimentally.
Learning inverse folding from millions of predicted structures [code][preprint]
Like ProteinMPNN, this is an inverse folding method that attempts to go from a structure (protein backbone atom coordinates) to a sequence. While ProteinMPNN was trained on experimentally determined strutures, this method, called ESM-IF, uses 12 million structures predicted by AlphaFold as its training material.
A Deep Learning Model for Predicting Tumor Suppressor Genes and Oncogenes from PDB Structure [github][bioRxiv preprint]
The authors use CNNs on feature maps extracted from protein 3D structures in the Protein Data Base (PDB) to predict oncogenes and tumor-suppressor genes.
Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning [code][bioRxiv preprint]
Predicts RNA-binding proteins using CNNs.
EVOVAE: Variational autoencoding of Protein Sequences[code][arXiv preprint]
From the abstract: "We present an embedding of natural protein sequences using a Variational Auto-Encoder and use it to predict how mutations affect protein function. We use this unsupervised approach to cluster natural variants and learn interactions between sets of positions within a protein. This approach generally performs better than baseline methods that consider no interactions within sequences, and in some cases better than the state-of-the-art approaches that use the inverse-Potts model. This generative model can be used to computationally guide exploration of protein sequence space and to better inform rational and automatic protein design."
Structure-Based Function Prediction using Graph Convolutional Networks [preprint]
From the abstract: "We present a deep learning Graph Convolutional Network (GCN) trained on sequence and structural data and evaluate it on ~40k proteins with known structures and functions from the Protein Data Bank (PDB). Our GCN predicts functions more accurately than Convolutional Neural Networks trained on sequence data alone and competing methods. Feature extraction via a language model removes the need for constructing multiple sequence alignments or feature engineering. Our model learns general structure-function relationships by robustly predicting functions of proteins with ≤ 30% sequence identity to the training set. Using class activation mapping, we can automatically identify structural regions at the residue-level that lead to each function prediction for every protein confidently predicted, advancing site-specific function prediction."
This category is divided into several subfields.
DeepVariant [github][preprint]
This preprint from Google originally came out in late 2016 but it got the most publicity about a year later when the code was made public and press releases started appearing. The Google researchers approached a well-studied problem, variant calling from DNA sequencing data (where the aim is to correctly identify variations from the reference genome in an individual's DNA, e.g. mutations or polymorphisms) using a counter-intuitive but clever approach. Instead of using the nucleotides in the sequenced DNA fragments directly (in the form of the symbols A, C, G, T), they first converted the sequences into images and then applied convolutional neural networks to these images (which represent "pile-ups" or DNA sequences; stacks of aligned sequences.) This turned out to be a very effective way to call variants as proven by both Google's own and independent benchmarks.
Language models enable zero-shot prediction of the effects of mutations on protein function [github][preprint]
This work builds on Meta's protein language models (ESM-1 et al.; see above) and shows that these models can be used for "zero-shot" prediction of variant effects on protein function; that is, no extra experimental data or model training is needed. The protein language model can be used as-is to infer variant effects.
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.
Gene Expression Convolutions Using Gene Interaction Graphs [github] [arxiv] They discuss how gene-gene interaction graphs (same pathway, protein-protein, co-expression, or research paper text association) can be used to impose a bias on a deep neural network model similar to the spatial bias imposed by convolutions on an image. They find this approach provides an advantage for particular tasks in a low data regime but is very dependent on the quality of the graph used.
ADAGE – Analysis using Denoising Autoencoders of Gene Expression [github]
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.)
Exploiting Ladder Networks for Gene Expression Classification [paper]
This paper applies Ladder networks, a semi-supervised deep learning method, to the binary cancer classification problem. The model performance is evaluated on TCGA dataset against other deep learning and conventional machine learning approaches.
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.)
Gene expression inference with deep learning [github][paper]
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.
Boosting Gene Expression Clustering with System-Wide Biological Information: A Robust Autoencoder Approach [bioRxiv preprint]
Uses a robust autoencoder (an autoencoder with an outlier filter) to cluster gene expression profiles.
Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk [github][paper]
The authors use a two-step model to predict the effect of genetic variants on gene expression. In the first step, the authors trained a convolutional neural network to model the 2002 epigenetic marks collected in ENCODE and ROADMAP consortium. In the second step, the authors trained a tissue-specific regularized linear model on the cis-regulatory region of the gene that is encoded by the first step convolutional neural network model. Then the effect of the variants on tissue-specific gene is calculated by the decrease in predicted gene expression through in silico mutagenesis.
Transcriptomic learning for digital pathology [preprint]
From the abstract: "We propose a novel approach based on the integration of multiple data modes, and show that our deep learning model, HE2RNA, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without the need for expert annotation. HE2RNA is interpretable by design, opening up new opportunities for virtual staining. In fact, it provides virtual spatialization of gene expression,as validated by double-staining on an independent dataset. Moreover, the transcriptomic representation learned by HE2RNA can be transferred to improve predictive performance for other tasks, particularly for small datasets."
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 for pointing out some of these.
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences [github]
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.
Basset – learning the regulatory code of the accessible genome with deep convolutional neural networks [github]
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.)
Basenji – Sequential regulatory activity prediction across chromosomes with convolutional neural networks [github1][github2][biorxiv]
A follow-up project to Basset, this Tensorflow-based model uses both standard and dilated convolutions to model regulatory signals and gene expression (in the form of CAGE tag density) in many different cell types. Notably, the underlying model has been brought into Google's Tensor2Tensor repository (see "github2" link above), which collects many models in image and speech recognition, machine translation, text classification etc. However, at the time of writing the Tensor2Tensor model seems not quite mature for easy use, so it is probably better to use the dedicated Basenji repo ("github1") for now.
DeepSEA – Predicting effects of noncoding variants with deep learning–based sequence model [web server][paper]
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).
DeepBind – Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning [code][paper]
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.)
DeeperBind - Enhancing Prediction of Sequence Specificities of DNA Binding Proteins [preprint]
This is an attempt to improve on DeepBind by adding a recurrent sequence learning module (LSTM) after the convolutional layer(s). In this way, the authors propose to capture a positional dimension that is lost in the pooling step in the original DeepBind design. They claim that benchmarking shows that this architecture leads to superior performance compared to previous work.
DeepMotif - Visualizing Genomic Sequence Classifications [paper]
This is also about learning and predicting binding specificities of proteins to certain DNA patterns or "motifs". However, this paper makes use of a combination of convolutional layers and highway networks, with more layers than the DeepBind network. The authors also show how a learned classifier can generate typical DNA motifs by input optimization; applying back-propagation with all the weights held constant in order to find an input pattern that maximally activates the appropriate output node in the network.
Convolutional Neural Network Architectures for Predicting DNA-Protein Binding [code][paper]
This work describes a systematic exploration of convolutional neural network (CNN) architectures for DNA-protein binding. It concludes that the convolutional kernels are very important for the success of the networks on motif-based tasks. Interestingly, the authors have provided a Dockerized implementation of DeepBind from the Frey lab (see above) and also provide EC2-laucher scripts and code for comparing different GPU enabled models programmed in Caffe.
PEDLA: predicting enhancers with a deep learning-based algorithmic framework [code][paper]
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 [paper][code]
An ensemble prediction method for 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.
FIDDLE: An integrative deep learning framework for functional genomic data inference [paper][code][Youtube talk]
The group predicted transcription start site and regulatory regions but claims this solution could be easily generalized and predict other features too. FIDDLE stands for Flexible Integration of Data with Deep LEarning. The idea (nicely explained by the author in the YouTube video above) is to model several genomic signals jointly using convolutional networks. This could be for example DNase-seq, ATAC-seq, ChIP-seq, TSS-seq, maybe RNA-seq signals (as in .wig files with one value per base in the genome).
Deep Learning Of The Regulatory Grammar Of Yeast 5′ Untranslated Regions From 500,000 Random Sequences [paper][code]
This is a CNN model that attempts to predict protein expression from the DNA sequence in a specific type of genomic region called 5' UTR (five-prime untranslated region). The model is built in Keras and a nice touch by the authors is that they optimized the parameters using hyperopt, which is also shown in one of the Jupyter notebooks that comes along with the paper. The results look promising and easily reproducible, judging from my own trial.
Modeling Enhancer-Promoter Interactions with Attention-Based Neural Networks [bioRxiv preprint][code]
The concept of attention in (recurrent) neural networks has become quite popular recently, not least because it has been used to great effect in machine translation models. This paper proposes an attention-based model for getting at the interactions between enhancer sequences and promoter sequences.
Predicting Transcription Factor Binding Sites with Convolutional Kernel Networks [bioRxiv preprint][code]
This paper uses a hybrid of CNNs (to learn good representations) and kernel methods (to learn good prediction functions) to predict transcription factor binding sites.
Predicting DNA accessibility in the pan-cancer tumor genome using RNA-seq, WGS, and deep learning [bioRxiv preprint]
Like Basset (above) this paper shows how to predict DNA accessibility from sequence using CNNs, but it adds the possibility to leverage RNA sequencing data from different cell types as input. In this way implicit information related to cell type can be "transferred" to the accessibility prediction task.
Deep learning at base-resolution reveals motif syntax of the cis-regulatory code [bioRxiv preprint]
Here, a CNN with dilated convolutions is used to learn how different transcription factor binding motifs cooperate. This is the "motif syntax" mentioned in the title. The neural network is trained to predict the signal from a basepair-resolution ChIP assay (ChIP-nexus) and the trained network is then used to infer rules of motif cooperativity.
DNA language models are powerful zero-shot predictors of genome-wide variant effects [github][preprint]
A BERT-like DNA language models, complemented with dilated convolutions to process the raw one-hot encoded inputs but keeping the sequence length fixed, is trained on several different plant genomes and shown to be able to predict variant effects in Arabidopsis thaliana. The authors claim that the model, called Genomic Pretrained Network (GPN), outperforms predictors based on popular conservation scores such as phyloP and phastCons.
DeepLNC, a long non-coding RNA prediction tool using deep neural network [paper] [web server]
Identification of potential long non-coding RNA molecules from DNA sequence, based on k-mer profiles.
A Deep Recurrent Neural Network Discovers Complex Biological Rules to Decipher RNA Protein-Coding Potential [github][paper]
From the abstract: While traditional, feature-based methods for RNA classification are limited by current scientific knowledge, deep learning methods can independently discover complex biological rules in the data de novo. We trained a gated recurrent neural network (RNN) on human messenger RNA (mRNA) and long noncoding RNA (lncRNA) sequences. Our model, mRNA RNN (mRNN), surpasses state-of-the-art methods at predicting protein-coding potential.
DeepCpG - Predicting DNA methylation in single cells [paper] [code] [docs]
DeepCpG is a deep neural network for predicting DNA methylation in multiple cells. DeepCpG has a modular architecture, consisting of a recurrent CpG module to account for correlations between CpG sites within and across cells, a convolutional DNA module to extract patterns from a wide DNA sequence window, and a Joint module that integrates the evidence from the CpG and DNA module to predict the methylation state of multiple cells for a target CpG site. DeepCpG yields accurate predictions, enables discovering DNA sequence motifs that are associated with DNA methylation states and cell-to-cell variability, and can be used for analyzing the effect of single-nucleotide mutations on DNA methylation. DeepCpG is implemented in Python and publicly available.
Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks [paper][web server]
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.
DeepCpG - Predicting DNA methylation in single cells [paper] [code] [docs]
See above.
CellCnn – Representation Learning for detection of disease-associated cell subsets [code][paper]
This is a convolutional network (Lasagne/Theano) based approach for “Representation Learning for detection of phenotype-associated cell subsets.” 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.
DeepCyTOF: Automated Cell Classification of Mass Cytometry Data by Deep Learning and Domain Adaptation[paper]
Describes autoencoder approaches (stacked AE and multi-AE) to gating (assigning cells into discrete groups) with mass cytometry (CyTOF).
Using Neural Networks To Improve Single-Cell RNA-Seq Data Analysis[preprint]
Tests a variety of neural network architectures for obtaining a reduced representation of single-cell gene expression data. Introduces a database of tens of thousands of single-cell profiles which can be queried to infer a cell type or state based on this reduced representation.
Removal of batch effects using distribution-matching residual networks[code][paper]
Most high-throughput assays in genomics, proteomics etc. are affected to some extent by systematic technical errors, so-called "batch effects". This paper uses a residual neural network to attenuate batch effects by trying to match the distributions of replicate experiments on e.g. single-cell RNA sequencing or mass cytometry.
Active deep learning reduces annotation burden in automatic cell segmentation [bioRxiv preprint]
Active learning, a framework addressing how to select training examples in order to train a model most efficiently, is shown to significantly reduce the time required by experts to annotate cell segmentation images in high-throughput high-context microscopy. Training deep learning models on this type of application of course requires a lot of high-quality labeled data, but the time of the human experts that can provide the labels (perform annotation) is limited and expensive.
scVAE: Variational auto-encoders for single-cell gene expression data [code][preprint]
This approach models single-cell gene expression data directly from counts without initial normalization, and performs clustering in the latent space. Since it is based on a variational autoencoder, it can also be used to generate synthetic single-cell data by sampling from the latent distribution.
CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets [code][preprint]
The authors present a generative model for removing statistical background noise in single-cell RNA-seq datasets.
scVAE: Single-cell variational auto-encoders [code][preprint]
scVAE is a command-line tool for modelling single-cell transcript counts using variational auto-encoders. Using variational autoencoders it is possible both to model the data in a more compact way and to generate realistic synthetic data based on the distribution that the real data come from.
Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks [code][preprint]
From the abstract: "A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here we propose the use of conditional single cell Generative Adversarial Neural Networks (cscGANs) for the realistic generation of single cell RNA-seq data. cscGANs learn non-linear gene-gene dependencies from complex, multi cell type samples and use this information to generate realistic cells of defined types."
Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data [code][preprint]
From the abstract: "Deep learning has emerged as a powerful methodology for predicting a variety of complex biological phenomena. However, its utility for biological discovery has so far been limited, given that generic deep neural networks provide little insight into the biological mechanisms that underlie a successful prediction. Here we demonstrate deep learning on biological networks, where every node has a molecular equivalent (such as a protein or gene) and every edge has a mechanistic interpretation (e.g., a regulatory interaction along a signaling pathway). With knowledge-primed neural networks (KPNNs), we exploit the ability of deep learning algorithms to assign meaningful weights to multi-layered networks for interpretable deep learning."
Learning substructure invariance for out-of-distribution molecular representations [github][paper]
A general molecular representation learning framework entitled MoleOOD which can incorporate any existing molecular representation learning method as backbone to improve their generalization ability against distribution shifts. Specifically, MoleOOD devises a new learning scheme with its equivalent practical instantiation. MoleOOD also develops an environment inference model to identify each molecule’s corresponding environment without need of manual specifications of environments.
Neural graph fingerprints [github]
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 used to co-host the very good Talking Machines podcast.
Automatic chemical design using a data-driven continuous representation of molecules [github][preprint]
Abstract starts: "We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds."
Objective-Reinforced Generative Adversarial Networks (ORGAN) [github][preprint]
A method that combines generative models with reinforcement learning to direct the generative process towards some desired target, ORGAN is a generic method for discrete data but is in this case exemplified by a drug discovery use case.
Extraction of organic chemistry grammar from unsupervised learning of chemical reactions [github][paper]
This package does atom mapping for chemistry using transformer networks. From the abstract: During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of “reaction rules” from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions.
Molecular De-Novo Design through Deep Reinforcement Learning [github][preprint]
PyTorch sequence generation model that uses reinforcement learning. Nice widget showing training progress and molecules generated during training is shown on the Github page. Abstract starts: "This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target."
One-shot learning models for drug discovery and DeepChem [github][Python library][paper]
DeepChem is a "... [P]ython library that aims to make the use of machine-learning in drug discovery straightforward and convenient" which checks a lot of boxes when it comes to advanced deep learning: one-shot learning, graph convolutional networks, learning from less data, and LSTM embeddings. According to the GitHub site, "DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, and quantum chemistry."
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology [github][paper]
Explores the use of generative adversarial networks (GAN) in generating new molecular leads for drug candidates. In analogy to generating images or video that "look like" they come from some specified distribution, perhaps with some conditioning like "show me a cat picture", the authors reason that novel drug-like molecular structures can be generated with cues about what kind of drug one wants. Here they explore a specific type of generative network, an adversarial autoencoder (AAE), and adapt it into what they call a "artificially-intelligent drug discovery engine."
Deep learning enables rapid identification of potent DDR1 kinase inhibitors [github][paper] In this paper from InSilico Medicine, which came out to some fanfare in 2019, an approach called GENTRL (Generative Tensorial Reinforcement Learning) was used to do rapid discovery of small-molecule inhibitors towards an interesting target. Using this method, the authors were able to come up with a candidate molecule in just 21 days. The model uses an initial generative step with a variational autoencoder and a reinforcement learning procedure for exploring the chemical space. They use an interesting loss function based on Kohonen self-organizing maps. Tensor decomposition was used to encode the relationship between chemical structures and properties.
Deep Genomics Nominates Industry’s First AI-Discovered Therapeutic Candidate [preprint]
In September 2019, Deep Genomics announced that its deep learning-based platform had identified a therapeutic target and a corresponding drug candidate. The details of the disease-causing mechanism targeted by the proposed candidate molecule are in the preprint link above.
Deep biomarkers of human aging [online predictor][paper]
From the abstract: "One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. "
Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data [code][paper]
Classification algorithms for metabolomics data with respect to estrogen receptor status are compared, and the best performing algorithm is an autoencoder-based feedforward network with parameters tuned using H2O's R interface.
In many cases, it can be useful to generate synthetic data that resembles real data in order to boost dataset sizes or avoid violating patient privacy. Here, some of these approaches are listed.
Privacy-preserving generative deep neural networks support clinical data sharing [Github][bioRxiv preprint]
This describes a clever idea where generative adversarial networks (GANs) are used to synthesize data that closely resembles actual data measured on study participants, but which cannot be traced back to a specific subject. The latter aspect, called differential privacy, is incorporated into the method by design and gives strong guarantees of the likelihood that a subject could be identified as a member of a trial.
Creating artificial human genomes using generative models [preprint]
The authors compare Restricted Boltzmann Machines (RBM) and Generative Adversarial Networks (GAN) as tools for creating synthetic human genomes.
Deep learning for population genetic inference [code][paper]
Diet networks: thin parameters for fat genomics [manuscript]
This weirdly-named paper addresses the frequently encountered problem in genomics where the number of features is much larger than the number of training examples. Here, it is addressed in the context of SNPs (single-nucleotide polymorphisms, genetic variations between individuals). The authors propose a new network parametrization that reduces the number of free parameters using a multi-task architecture which tries to learn a useful embedding of the input features.
Using deep learning to model the hierarchical structure and function of a cell [web server][paper]
In this ambitious paper, the authors attempt to construct an interpretable neural network model (VNN; visible neural network) of a eukaryotic cell based on millions of genotype-phenotype associations. The network is built in a hierarchy with 12 levels, where each level is supposed to reflect a biologically meaningful level of organization. The resulting model can predict, for a given genetic perturbation, what the resulting phenotype is likely to be.