An article published in GenomeBiology presents Saluki: a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life.
], ReLU activation, 1D convolution with kernel width 5, dropout, and max pooling with width 2. Overall, the model consists of 155,521 learnable parameters. We chose layer normalization over batch normalization because most of the 3′ positions are zero padded and would confuse the batch statistics. In contrast, layer normalization is computed independently at each position and simply maintains a zero vector for the padded regions.
To make a single numeric prediction for each sequence, we must aggregate information across the variable lengths. To achieve this, we use a common recurrent neural network block called the gated recurrent unit []. After layer normalization and ReLU activation, the GRU runs backward from the often-padded 3′ end to the information dense 5′ end. We take the final GRU hidden representation from the most 5′ position as a summary of the entire sequence.
We trained with the MSE loss function using the Adam optimizer on batches of 64 examples and learning rate 0.001, beta1 0.9, and beta2 0.98. We clipped gradients to a global norm of 0.5. We used dropout probability 0.3 throughout and added L2 regularization on all convolution, GRU, and dense layer weights with coefficient 0.001. We used skopt to optimize these hyperparameters as well as the number of channels throughout the model.
Our ten folds of genes allowed us to train multiple models, in which each fold was held out as a test set . Because we observed variance from training run to training run, we trained five replicate models for each held-out test fold, producing a total of fifty trained parameter settings. After preliminary analyses to examine the predictions’ variance, we averaged the predictions of the five replicates per test set as an ensemble, which improves accuracy and robustness.
For mutations that modify stop codons, we optionally set the coding track 3′ onwards to zeros. This mode creates disproportionately large effect predictions for these mutations, which can be inconvenient for analyses focused on alternative aspects. We therefore decided not to modify the coding track downstream of the stop codon.] on each functional region independently to identify the enriched motifs associated with changes in half-life.
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