A Comparative Study of Various Deep Learning Architectures for 8-state Protein Secondary Structures Prediction

2020 ◽  
pp. 501-513
Author(s):  
Moheb R. Girgis ◽  
Enas Elgeldawi ◽  
Rofida Mohammed Gamal
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Secondary Structures ◽  
Protein Secondary Structures ◽  
Learning Architectures

scholarly journals Multi-task deep learning for concurrent prediction of protein structural properties

2021 ◽  
Author(s):  
Buzhong Zhang ◽  
Jinyan Li ◽  
Lijun Quan ◽  
Qiang Lyu
Keyword(s):  
Deep Learning ◽  
Structural Properties ◽  
Solvent Accessibility ◽  
Close Association ◽  
Protein Structures ◽  
Secondary Structures ◽  
Structural Features ◽  
New Method ◽  
Machine Learning Techniques ◽  
Protein Secondary Structures

AbstractProtein structural properties are diverse and have the characteristics of spatial hierarchy, such as secondary structures, solvent accessibility and backbone angles. Protein tertiary structures are formed in close association with these features. Separate prediction of these structural properties has been improved with the increasing number of samples of protein structures and with advances in machine learning techniques, but concurrent prediction of these tightly related structural features is more useful to understand the overall protein structure and functions. We introduce a multi-task deep learning method for concurrent prediction of protein secondary structures, solvent accessibility and backbone angles (ϕ, ψ). The new method has main two deep network modules: the first one is designed as a DenseNet architecture a using bidirectional simplified GRU (GRU2) network, and the second module is designed as an updated Google Inception network. The new method is named CRRNN2.CRRNN2 is trained on 14,100 protein sequences and its prediction performance is evaluated by testing on public benchmark datasets: CB513, CASP10, CASP11, CASP12 and TS1199. Compared with state-of-the-art methods, CRRNN2 achieves similar, or better performance on the prediction of 3- and 8-state secondary structures, solvent accessibility and backbone angles (ϕ, ψ). Online CRRN-N2 applications, datasets and standalone software are available at http://qianglab.scst.suda.edu.cn/crrnn2/.

A comparative study of deep learning architectures on melanoma detection

2019 ◽  
Vol 58 ◽  
pp. 76-83 ◽  
Author(s):  
Sara Hosseinzadeh Kassani ◽  
Peyman Hosseinzadeh Kassani
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Melanoma Detection ◽  
Learning Architectures

scholarly journals Protein Secondary Structure Prediction With a Reductive Deep Learning Method

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiliang Lyu ◽  
Zhijin Wang ◽  
Fangfang Luo ◽  
Jianwei Shuai ◽  
Yandong Huang
Keyword(s):  
Deep Learning ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Resonance Spectroscopy ◽  
Data Set ◽  
X Ray Crystallography ◽  
Protein Secondary Structures

Protein secondary structures have been identified as the links in the physical processes of primary sequences, typically random coils, folding into functional tertiary structures that enable proteins to involve a variety of biological events in life science. Therefore, an efficient protein secondary structure predictor is of importance especially when the structure of an amino acid sequence fragment is not solved by high-resolution experiments, such as X-ray crystallography, cryo-electron microscopy, and nuclear magnetic resonance spectroscopy, which are usually time consuming and expensive. In this paper, a reductive deep learning model MLPRNN has been proposed to predict either 3-state or 8-state protein secondary structures. The prediction accuracy by the MLPRNN on the publicly available benchmark CB513 data set is comparable with those by other state-of-the-art models. More importantly, taking into account the reductive architecture, MLPRNN could be a baseline for future developments.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

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