Computational identification of human ubiquitination sites using convolutional and recurrent neural networks

2021 ◽  
Author(s):  
Xiaofeng Wang ◽  
Renxiang Yan ◽  
Yongji Wang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Human Diseases ◽  
Post Translational Modification ◽  
Important Protein ◽  
Computational Identification

Ubiquitination is a very important protein post-translational modification in human, which is closely related to many human diseases such as cancers. Although some methods have been elegantly proposed to predict...

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

LatticeRnn: Recurrent Neural Networks Over Lattices

2016 ◽  
Author(s):  
Faisal Ladhak ◽  
Ankur Gandhe ◽  
Markus Dreyer ◽  
Lambert Mathias ◽  
Ariya Rastrow ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks

Joint Learning of Correlated Sequence Labeling Tasks Using Bidirectional Recurrent Neural Networks

2017 ◽  
Author(s):  
Vardaan Pahuja ◽  
Anirban Laha ◽  
Shachar Mirkin ◽  
Vikas Raykar ◽  
Lili Kotlerman ◽  
...  
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Joint Learning ◽  
Sequence Labeling
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