Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction

By Pietro Di Lena, Pierre Baldi, Ken Nagata. 2012

45 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Mirco Michel
2. Arne Elofsson
3. Marcin J. Skwark
4. Chensi Cao
5. Hai Tan
6. Deshou Song
7. Wenjie Shu
8. Weizhong Li

Citations in the corpus

• Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014, background): “In general, deep learning is very useful in structured learning problems including protein contact predictions [30].”
• Deep Learning and Its Applications in Biomedicine (2018, methodology): “For example, Lena et al. introduced a deep spatio-temporal learning architecture, achieved an accuracy roughly 10% higher than other methods [181], and Zhou et al. presented a deep supervised and convolutional generative stochastic network, achieving an accuracy of 66.4% [182].”
• Deep learning in bioinformatics (2016, unknown): “In addition, many other emergent deep learning architectures have been suggested, such as deep spatio-temporal neural networks (DST-NNs) [38], multidimensional recurrent neural networks (MD-RNNs) [39] and convolutional auto-encoders (CAEs) [40, 41].”