Deep Learning Models Based on Distributed Feature Representations for Alternative Splicing Prediction
Deep Learning Models Based on Distributed Feature Representations for Alternative Splicing Prediction is a scholarly work, published in 2018 in ''IEEE Access''. The main subjects of the publication include deep learning, artificial intelligence, methylation, feature, RNA splicing, ribosome, feature learning, machine learning, computational biology, and computer science. The authors propose a convolutional neural network and multilayer perceptron models to tackle the AS prediction task as classification and regression.