Improving protein disorder prediction by deep bidirectional long short‐term memory recurrent neural networks
Sign Up to like & getrecommendations! Published in 2017 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btw678
Abstract: Motivation: Capturing long‐range interactions between structural but not sequence neighbors of proteins is a long‐standing challenging problem in bioinformatics. Recently, long short‐term memory (LSTM) networks have significantly improved the accuracy of speech and image classification… read more here.
Keywords: neural networks; disorder; disorder prediction; recurrent neural ... See more keywords
Seismic Data Reconstruction Using Deep Bidirectional Long Short-Term Memory With Skip Connections
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2020.2993847
Abstract: Due to environmental and economic constraints on their acquisition, seismic data are always irregularly sampled and include bad or missing traces, which can cause problems for seismic data processing. Recently, many researchers have attempted to… read more here.
Keywords: data reconstruction; seismic data; reconstruction using; deep bidirectional ... See more keywords
Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network
Sign Up to like & getrecommendations! Published in 2018 at "Biomolecules"
DOI: 10.3390/biom8020033
Abstract: Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we… read more here.
Keywords: stacked deep; network; accessibility; deep bidirectional ... See more keywords