scholarly journals DeepDBP: Deep Neural Networks for Identification of DNA-binding Proteins

2019 ◽  
Author(s):  
Shadman Shadab ◽  
Md Tawab Alam Khan ◽  
Nazia Afrin Neezi ◽  
Sheikh Adilina ◽  
Swakkhar Shatabda
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dna Binding ◽  
Binding Proteins ◽  
Deep Neural Networks ◽  
Defense Mechanism ◽  
Dna Binding Proteins ◽  
Cellular Level ◽  
Test Accuracy ◽  
The Past

AbstractDNA-Binding proteins (DBP) are associated with many cellular level functions which includes but not limited to body’s defense mechanism and oxygen transportation. They bind DNAs and interact with them. In the past DBPs were identified using experimental lab based methods. However, in the recent years researchers are using supervised learning to identify DBPs solely from protein sequences. In this paper, we apply deep learning methods to identify DBPs. We have proposed two different deep learning based methods for identifying DBPs: DeepDBP-ANN and DeepDBP-CNN. DeepDBP-ANN uses a generated set of features trained on traditional neural network and DeepDBP-CNN uses a pre-learned embedding and Convolutional Neural Network. Both of our proposed methods were able to produce state-of-the-art results when tested on standard benchmark datasets.DeepDBP-ANN had a train accuracy of 99.02% and test accuracy of 82.80%.And DeepDBP-CNN though had train accuracy of 94.32%, it excelled at identifying test instances with 84.31% accuracy. All methods are available codes and methods are available for use at: https://github.com/antorkhan/DNABinding.

scholarly journals Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11262
Author(s):  
Guobin Li ◽  
Xiuquan Du ◽  
Xinlu Li ◽  
Le Zou ◽  
Guanhong Zhang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Dna Binding ◽  
Binding Proteins ◽  
Prediction Models ◽  
Short Term Memory ◽  
Local Features ◽  
Dna Binding Proteins ◽  
Superior Performance

DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

scholarly journals The artificial intelligence renaissance: deep learning and the road to human-Level machine intelligence

2018 ◽  
Vol 7 ◽  
Author(s):  
Kar-Han Tan ◽  
Boon Pang Lim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Machine Intelligence ◽  
Algorithmic Problem ◽  
Network Architectures ◽  
The Road ◽  
The Past ◽  
Research Challenges

In this paper we look at recent advances in artificial intelligence. Decades in the making, a confluence of several factors in the past few years has culminated in a string of breakthroughs in many longstanding research challenges. A number of problems that were considered too challenging just a few years ago can now be solved convincingly by deep neural networks. Although deep learning appears to be reducing the algorithmic problem solving to a matter of data collection and labeling, we believe that many insights learned from ‘pre-Deep Learning’ works still apply and will be more valuable than ever in guiding the design of novel neural network architectures.

scholarly journals On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0188129 ◽  
Author(s):  
Yu-Hui Qu ◽  
Hua Yu ◽  
Xiu-Jun Gong ◽  
Jia-Hui Xu ◽  
Hong-Shun Lee
Keyword(s):  
Deep Learning ◽  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Learning Approach

scholarly journals DeepDBP: Deep neural networks for identification of DNA-binding proteins

2020 ◽  
Vol 19 ◽  
pp. 100318 ◽  
Author(s):  
Shadman Shadab ◽  
Md Tawab Alam Khan ◽  
Nazia Afrin Neezi ◽  
Sheikh Adilina ◽  
Swakkhar Shatabda
Keyword(s):  
Neural Networks ◽  
Dna Binding ◽  
Binding Proteins ◽  
Deep Neural Networks ◽  
Dna Binding Proteins

scholarly journals Fastened CROWN: Tightened Neural Network Robustness Certificates

2020 ◽  
Vol 34 (04) ◽  
pp. 5037-5044
Author(s):  
Zhaoyang Lyu ◽  
Ching-Yun Ko ◽  
Zhifeng Kong ◽  
Ngai Wong ◽  
Dahua Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Programming ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Convex Relaxation ◽  
Real Life ◽  
Network Robustness ◽  
The Past ◽  
Computationally Expensive

The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reliable evaluations of the fragility level in different deep neural networks. Apart from devising adversarial attacks, quantifiers that certify safeguarded regions have also been designed in the past five years. The summarizing work in (Salman et al. 2019) unifies a family of existing verifiers under a convex relaxation framework. We draw inspiration from such work and further demonstrate the optimality of deterministic CROWN (Zhang et al. 2018) solutions in a given linear programming problem under mild constraints. Given this theoretical result, the computationally expensive linear programming based method is shown to be unnecessary. We then propose an optimization-based approach FROWN (Fastened CROWN): a general algorithm to tighten robustness certificates for neural networks. Extensive experiments on various networks trained individually verify the effectiveness of FROWN in safeguarding larger robust regions.

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