• Corpus ID: 51972549

GL-BLSTM: a novel structure of bidirectional long-short term memory for disulfide bonding state prediction

@article{Jiang2018GLBLSTMAN,
  title={GL-BLSTM: a novel structure of bidirectional long-short term memory for disulfide bonding state prediction},
  author={Junshu Jiang and Shang-shu Zou and Yu Sun and Shengxiang Zhang College of Life Sciences and Southeast University and Guangzhou and Guangdong and China Department of Mathematics and Informatics and China.},
  journal={arXiv: Quantitative Methods},
  year={2018}
}
Background: Disulfide bonds are crucial to protein structural formation. Developing an effective method topredict disulfide bonding formation is important for protein structural modeling and functional study. Mostcurrent methods still have shortcomings, including low accuracy and strict requirements for the selection ofdiscriminative features. Results: In this study, we introduced a nested structure of Bidirectional Long-short Term Memory(BLSTM)neural network called Global-Local-BLSTM (GL-BLSTM… 
Snails In Silico: A Review of Computational Studies on the Conopeptides
TLDR
A review of different computational strategies that have been applied to understanding and predicting conopeptides structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding.
Static Malware Detection using Recurrent Neural Networks
An ever-growing number of malicious attacks on our IT infrastructure calls for new and better methods of protection. In this thesis, we focus on the use of recurrent neural networks as an agile and

References

SHOWING 1-10 OF 30 REFERENCES
Dinosolve: a protein disulfide bonding prediction server using context-based features to enhance prediction accuracy
TLDR
The computational results have shown that the context-based scores are effective features to enhance the prediction accuracies of both disulfide bonding state prediction and connectivity prediction.
Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure
TLDR
This work has developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program.
Protein Secondary Structure Prediction with Long Short Term Memory Networks
TLDR
This work uses a bidirectional recurrent neural network with long short term memory cells for prediction of secondary structure from the amino acid sequence and reports better performance than state of the art on the secondary structure 8-class problem.
Predicting the state of cysteines based on sequence information.
TLDR
The result shows that among these protein descriptors; evolution information is the most important feature for representing the disulfide-containing proteins.
Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks.
TLDR
A hybrid system (hidden neural network) based on a hidden Markov model (HMM) and neural networks (NN) was trained to predict the bonding states of cysteines in proteins starting from the residue chains, which can correctly predict 84% of the chains in the data set.
Improving disulfide connectivity prediction with sequential distance between oxidized cysteines
TLDR
Improvements demonstrate that DOC, with a proper scaling scheme, is an effective feature for the prediction of disulfide connectivity, which helps towards the solution of protein structure prediction.
Convolutional LSTM Networks for Subcellular Localization of Proteins
TLDR
This study demonstrates that LSTM networks predict the subcellular location of proteins given only the protein sequence with high accuracy, outperforming current state of the art algorithms.
Backbone statistical potential from local sequence-structure interactions in protein loops.
TLDR
A new statistical potential is presented for a reduced backbone representation that has both structure and sequence characteristics as variables and is able to identify with high accuracy the native structure of a loop with a given sequence among possible alternative conformations from sets of well-constructed decoys.
Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features
TLDR
A set of simple and physically motivated criteria for secondary structure, programmed as a pattern‐recognition process of hydrogen‐bonded and geometrical features extracted from x‐ray coordinates is developed.
Long Short-Term Memory
TLDR
A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
...
1
2
3
...