Deep architectures for protein contact map prediction

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

286 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Kristen Grauman
5. Andrew Zisserman
6. Xiaogang Wang
7. Trevor Darrell
8. Alan L. Yuille

In Molecular Biology

1. Jinbo Xu
2. Sheng Wang
3. Jianlin Cheng
4. Yang Zhang
5. Badri Adhikari
6. Arne Elofsson
7. Robin Pearce
8. Yaoqi Zhou

In Computational Biology & Bioinformatics

1. Jinbo Xu
2. Sheng Wang
3. Jianlin Cheng
4. Arne Elofsson
5. Robin Pearce
6. Yang Zhang
7. Badri Adhikari
8. Siqi Sun

Citations in the corpus

• Deep learning in neural networks: An overview (2014, background): “Also in 2012, a system consisting of growing deep FNNs and 2D-BRNNs (Di Lena et al., 2012) won the CASP 2012 contest on protein contact map prediction.”
• Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “In recent years, deeper architectures have been explored for contact prediction, such as CMAPpro [196], DNCON [197] and PConsC [198].”
• Distance-based protein folding powered by deep learning (2018, methodology): “Before ResNet, deep learning (DL) such as CMAPpro 18 and Deep Belief Networks (DBN) 19 were used for contact prediction, but ResNet is the first DL method that greatly outperforms DCA and shallow methods such as MetaPSICOV 20 .”
• A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “been effectively applied to residue-residue contact prediction and disorder prediction [27], [39], [40].”
• Folding membrane proteins by deep transfer learning (2017, methodology): “CMAPpro (Di Lena et al., 2012) uses a deep architecture, but its performance was worse than MetaPSICOV in CASP11 (Monastyrskyy et al., 2015).”
• Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns (2014, methodology): “PconsC2 performs better than earlier predictors In addition we compare PconsC2 to a set of alternative contact prediction methods, including mutual information (MI) and MI with phylogenetic correction (CMIr), and two recent machine learning based methods, PhyCMAP and CMAPpro.”
• Learning in the Machine: Random Backpropagation and the Deep Learning Channel (2016, background): “The question has gained further relevance due to the numerous successes achieved by backpropagation in a variety of problems ranging from computer vision [21,31,30,14] to speech recognition [12] in engineering, and from high energy physics [7,26] to biology [8,32,1] in the natural sciences, as well ”
• Pre-training of Recurrent Neural Networks via Linear Autoencoders (2014, unknown): “, [5, 6, 7].”