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.
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Ž.A. was supported by the German Bundesministerium für Bildung und Forschung (BMBF) through the project MechML (01IS18053F). The authors acknowledge M. Heinig and A. Raue for valuable feedback.
Nature Reviews Genetics thanks C. Greene and the other anonymous reviewer(s) for their contribution to the peer review of this work.
The authors declare no competing interests.
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Kaggle machine learning competitions: https://www.kaggle.com/sudalairajkumar/winning-solutions-of-kaggle-competitions
Keras model zoos: https://keras.io/applications/
PyTorch model zoos: https://pytorch.org/docs/stable/torchvision/models.html
An individual, measurable property or characteristic of a phenomenon being observed.
- Handcrafted features
Features derived from raw data (or other features) using manually specified rules. Unlike learned features, they are specified upfront and do not change during model training. For example, the GC content is a handcrafted feature of a DNA sequence.
- End-to-end models
Machine learning models that embed the entire data-processing pipeline to transform raw input data into predictions without requiring a preprocessing step.
- Deep neural networks
A wide class of machine learning models with a design that is loosely based on biological neural networks.
- Fully connected
Referring to a layer that performs an affine transformation of a vector followed by application of an activation function to each value.
Referring to a neural network layer that processes data stored in n-dimensional arrays, such as images. The same fully connected layer is applied to multiple local patches of the input array. When applied to DNA sequences, a convolutional layer can be interpreted as a set of position weight matrices scanned across the sequence.
Referring to a neural network layer that processes sequential data. The same neural network is applied at each step of the sequence and updates a memory variable that is provided for the next step.
- Graph convolutional
Referring to neural networks that process graph-structured data; they generalize convolution beyond regular structures, such as DNA sequences and images, to graphs with arbitrary structures. The same neural network is applied to each node and edge in the graph.
Unsupervised neural networks trained to reconstruct the input. One or more bottleneck layers have lower dimensionality than the input, which leads to compression of data and forces the autoencoder to extract useful features and omit unimportant features in the reconstruction.
- Generative adversarial networks
(GANs). Unsupervised learning models that aim to generate data points that are indistinguishable from the observed ones.
The desired output used to train a supervised model.
- Loss function
A function that is optimized during training to fit machine learning model parameters. In the simplest case, it measures the discrepancy between predictions and observations. In the case of quantitative predictions such as regression, mean-squared error loss is frequently used, and for binary classification, the binary cross-entropy, also called logistic loss, is typically used.
Character sequence of a certain length. For instance, a dinucleotide is a k-mer for which k = 2.
- Logistic regression
A supervised learning algorithm that predicts the log-odds of a binary output to be of the positive class as a weighted sum of the input features. Transformation of the log-odds with the sigmoid activation function leads to predicted probabilities.
- Sigmoid function
A function that maps real numbers to [0,1], defined as 1/(1 + e −x).
- Activation function
A function applied to an intermediate value x within a neural network. Activation functions are usually nonlinear yet very simple, such as the rectified-linear unit or the sigmoid function.
A strategy to prevent overfitting that is typically achieved by constraining the model parameters during training by modifying the loss function or the parameter optimization procedure. For example, the so-called L2 regularization adds the sum of the squares of the model parameters to the loss function to penalize large model parameters.
- Hidden layers
Layers are a list of artificial neurons that collectively represents a function that take as input an array of real numbers and returns an array of real numbers corresponding to neuron activations. Hidden layers are between the input and output layers.
- Rectified-linear unit
(ReLU). Widely used activation function defined as max(0, x).
The elementary unit of a neural network. An artificial neuron aggregates the inputs from other neurons and emits an output called activation. Inputs and activations of artificial neurons are real numbers. The activation of an artificial neuron is computed by applying a nonlinear activation function to a weighted sum of its inputs.
- Linear regression
A supervised learning algorithm that predicts the output as a weighted sum of the input features.
- Decision trees
Supervised learning algorithms in which the prediction is made by making a series of decisions of type ‘is feature i larger than x’ (internal nodes of the tree) and then predicting a constant value for all points satisfying the same decisions series (leaf nodes).
- Random forests
Supervised learning algorithms that train and average the predictions of many decision trees.
- Gradient-boosted decision trees
Supervised learning algorithms that train multiple decision trees in a sequential manner; at each time step, a new decision tree is trained on the residual or pseudo-residual of the previous decision tree.
- Position weight matrix
(PWM). A commonly used representation of sequence motifs in biological sequences. It is based on nucleotide frequencies of aligned sequences at each position and can be used for identifying transcription factor binding sites from DNA sequence.
The scenario in which the model fits the training set very well but does not generalize well to unseen data. Very flexible models with many free parameters are prone to overfitting, whereas models with many fewer parameters than the training data do not overfit.
Parameters of a convolutional layer. In the first layer of a sequence-based convolutional network, they can be interpreted as position weight matrices.
- Pooling operation
A function that replaces the output at a certain location with a summary statistic of the nearby outputs. For example, the max pooling operation reports the maximum output within a rectangular neighbourhood.
An axis other than one of the positional axes. For images, the channel axis encodes different colours (such as red, green and blue), for one-hot-encoded sequences (A: [1, 0, 0, 0], C: [0, 1, 0, 0] and so on), it denotes the bases (A, C, G and T), and for the output of the convolutions, it corresponds to the outputs of different filters.
- Dilated convolutions
Filters that skip some values in the input layers. Typically, each subsequent convolutional layer increases the dilation by a factor of two, thus achieving an exponentially increasing receptive field with each additional layer.
- Receptive field
The region of the input that affects the output of a convolutional neuron.
An array that stores the information of the patterns observed in the sequence elements previously processed by a recurrent neural network.
- Feature importance scores
The quantification values of the contributions of features to a current model prediction. The simplest way to obtain this score is to perturb the feature value and measure the change in the model prediction: the larger the change found, the more important the feature is.
An algorithm for computing gradients of neural networks. Gradients with respect to the loss function are used to update the neural network parameters during training.
- Saliency maps
Feature importance scores defined as the gradient absolute values of the model output with respect to the model input.
- Input-masked gradients
Feature importance scores defined as the gradient of the model output with respect to the model input multiplied by the input values.
- Automatic differentiation
A set of techniques, which consist of a sequence of elementary arithmetic operations, used to automatically differentiate a computer program.
- Model architecture
The structure of a neural network independent of its parameter values. Important aspects of model architecture are the types of layers, their dimensions and how they are connected to each other.
An unsupervised method for partitioning the observations into clusters by alternating between refining cluster centroids and updating cluster assignments of observations.
- Principal component analysis
An unsupervised learning algorithm that linearly projects data from a high-dimensional space to a lower-dimensional space while retaining as much variance as possible.
- t-Distributed stochastic neighbour embedding
(t-SNE). An unsupervised learning algorithm that projects data from a high-dimensional space to a lower-dimensional space (typically 2D or 3D) in a nonlinear fashion while trying to preserve the distances between points.
- Latent variable models
Unsupervised models describing the observed distribution by imposing latent (unobserved) variables for each data point. The simplest example is the mixture of Gaussian values.
- Bottleneck layer
A neural network layer that contains fewer neurons than previous and subsequent layers.
- Generative models
Models able to generate points from the desired distribution. Deep generative models are often implemented by a neural network that transforms samples from a standard distribution (normal and uniform) into samples from a complex distribution (gene expression levels or sequences that encode a splice site).
Parameters specifying the model or the training procedure that are not optimized by the learning algorithm (for example, by the stochastic gradient descent algorithm). Examples of hyperparameters are the number of layers, regularization strength, batch size and the optimization step size.
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Eraslan, G., Avsec, Ž., Gagneur, J. et al. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet 20, 389–403 (2019). https://doi.org/10.1038/s41576-019-0122-6
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