Fig. 4
From: Deep phenotyping: deep learning for temporal phenotype/genotype classification
The structure of an LSTM. The system at each time-point is updated based on the current input data, the status of the system at the previous time-point, and the content of the memory. Here, \(\phi\) and \(\sigma\) are hyperbolic tangent and sigmoid functions, respectively, and \(\odot\) stands for the element-wise multiplication. \(\mathbf{i}_t\), \(\mathbf{f}_t\), \(\mathbf{o}_t\) and \(\mathbf{c}(t)\) denote input gate, forget gate, output gate and memory cell respectively