scholarly journals Identification of Novel Therapeutic Targets in Myelodysplastic Syndrome Using Protein-Protein Interaction Approach and Neural Networks

2018 ◽  
Vol 11 (2) ◽  
Author(s):  
Ali A ◽  
Junaid M ◽  
Khan A ◽  
Kaushik AC ◽  
Mehmood A ◽  
...  
Keyword(s):  
Neural Networks ◽  
Myelodysplastic Syndrome ◽  
Protein Interaction ◽  
Therapeutic Targets ◽  
Protein Protein Interaction ◽  
Interaction Approach ◽  
Novel Therapeutic

scholarly journals A Shortest Dependency Path Based Convolutional Neural Network for Protein-Protein Relation Extraction

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Lei Hua ◽  
Chanqin Quan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Feature Selection ◽  
Protein Interaction ◽  
Kernel Methods ◽  
State Of The Art ◽  
Relation Extraction ◽  
Word Embedding ◽  
Protein Protein Interaction ◽  
Key Features

The state-of-the-art methods for protein-protein interaction (PPI) extraction are primarily based on kernel methods, and their performances strongly depend on the handcraft features. In this paper, we tackle PPI extraction by using convolutional neural networks (CNN) and propose a shortest dependency path based CNN (sdpCNN) model. The proposed method(1)only takes the sdp and word embedding as input and(2)could avoid bias from feature selection by using CNN. We performed experiments on standard Aimed and BioInfer datasets, and the experimental results demonstrated that our approach outperformed state-of-the-art kernel based methods. In particular, by tracking the sdpCNN model, we find that sdpCNN could extract key features automatically and it is verified that pretrained word embedding is crucial in PPI task.

scholarly journals Attention-based convolutional neural networks for protein-protein interaction site prediction

2021 ◽  
Author(s):  
Shuai Lu ◽  
Yuguang Li ◽  
Xiaofei Nan ◽  
Shoutao Zhang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Protein Interaction ◽  
Sliding Window ◽  
Protein Docking ◽  
Life Cycles ◽  
Supplementary Information ◽  
Interaction Site ◽  
Protein Protein Interaction ◽  
Neighboring Residue

Motivation: Protein-protein interactions are of great importance in the life cycles of living cells. Accurate prediction of the protein-protein interaction site (PPIs) from protein sequence improves our understanding of protein-protein interaction, contributes to the protein-protein docking and is crucial for drug design. However, practical experimental methods are costly and time-consuming so that many sequence-based computational methods have been developed. Most of those methods employ a sliding window approach, which utilize local neighbor information within a window size. However, they don't distinguish and use the effect of each individual neighboring residue at different position. Results: We propose a novel sequence-based deep learning method consisting of convolutional neural networks (CNNs) and attention mechanism to improve the performance of PPIs prediction. Our attention-based CNNs captures the different effect of each neighboring residue within a sliding window, and therefore making a better understanding of the local environment of target residue. We employ experiments on several public benchmark datasets. The experimental results demonstrate that our proposed method significantly outperforms the state-of-the-art techniques. We also analyze the difference using various sliding window lengths and amino acid residue features combination. Availability and implementation: The source code can be obtained from https://github.com/biolushuai/attention-based-CNNs-for-PPIs-prediction Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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