Search

• Explicitly modeling coding features helps prediction of TIS. • Integrating coding features with consensus motifs improves prediction of TIS. • CNN is sensitive to the first reading frame. Translation initiation site (TIS) prediction is one of the most crucial subtasks for gene annotation. Many computational methods have been proposed and achieved acceptable accuracy in transcripts (e.g., cDNA, mRNA). However, the prediction of TIS at the genome level is far more challenging and the computational methods for TIS prediction in genomic sequences so far reach modest performance. Recently, we proposed a method that improves the prediction of TIS in mRNA sequences and demonstrated the significance of explicitly modeling coding features. In this paper, we extend the same results to genomic sequence and present a two-stage deep learning model for TIS prediction in genomic sequence: the first stage to extract coding contrast features around TIS by a hybrid Convolutional Neural Network-Bidirectional Recurrent Neural Network architecture (Content-RCNN), and the second stage to integrate coding contrast features around TIS with TIS sequence encoded by one-hot encoding to jointly predict TIS by a CNN (Integrated-CNN). Four-fold cross validation tests on genome-wide human and mouse datasets demonstrate that the proposed model yields an improved prediction performance of TIS over existing state-of-the-art methods. The source code and the dataset used in the paper are publicly available at: https://github.com/xdcwei/DeepTIS/. [ABSTRACT FROM AUTHOR]

Fault prediction technology provides a way to reduce the loss caused by equipment failure. Currently, many efforts of failure prediction pay more attention to the fault trend and the remaining useful life. This paper devotes to predicting a specific failure mode of device in the future. We propose a two-step prediction method with two sub-models to predict the fault mode. The first model, named regression model, combines Attention-based-LSTM (Long Short-Term Memory) and Random Forest (RF) to predict time series trends, where wavelet packet analysis is adopted to extract elementary features of time series and form multivariable time series. Based on the status data forecasted by the first model, the second model, named classification model, combines Attention-based-LSTM and Extra-Tree (ET) to classify fault mode. The classified fault will occur in the device at some point in the future. In the regression model, Attention-based-LSTM performs deep level feature extraction of vibration sensor signals, and the result is fed to the last layer (i.e., an RF layer), which takes responsibility for predicting the future sequence. The future sequence is then provided to the classification model, where Attention-based-LSTM is used to extract implicit features, and ET classifier is used to accept these features and figure out the failure phenomenon. Our approach for predicting failure modes includes the severity and type of fault, which are rarely investigated synthetically. The experimental results of bearing data show that the sub-models can achieve high prediction accuracy and our method can predict the future failure mode with precision. [ABSTRACT FROM AUTHOR]