scholarly journals Classification of adaptor proteins using recurrent neural networks and PSSM profiles

2021 ◽  
Author(s):  
NQ Khanh Le ◽  
QH Nguyen ◽  
X Chen ◽  
S Rahardja ◽  
Binh Nguyen
Keyword(s):  
Neural Networks ◽  
Computational Biology ◽  
Recurrent Neural Networks ◽  
Protein Function ◽  
Intracellular Signaling ◽  
Protein Function Prediction ◽  
Characteristic Curve ◽  
Adaptor Proteins ◽  
The Neural Networks ◽  
Prediction Problems

© 2019 The Author(s). Background: Adaptor proteins are carrier proteins that play a crucial role in signal transduction. They commonly consist of several modular domains, each having its own binding activity and operating by forming complexes with other intracellular-signaling molecules. Many studies determined that the adaptor proteins had been implicated in a variety of human diseases. Therefore, creating a precise model to predict the function of adaptor proteins is one of the vital tasks in bioinformatics and computational biology. Few computational biology studies have been conducted to predict the protein functions, and in most of those studies, position specific scoring matrix (PSSM) profiles had been used as the features to be fed into the neural networks. However, the neural networks could not reach the optimal result because the sequential information in PSSMs has been lost. This study proposes an innovative approach by incorporating recurrent neural networks (RNNs) and PSSM profiles to resolve this problem. Results: Compared to other state-of-the-art methods which had been applied successfully in other problems, our method achieves enhancement in all of the common measurement metrics. The area under the receiver operating characteristic curve (AUC) metric in prediction of adaptor proteins in the cross-validation and independent datasets are 0.893 and 0.853, respectively. Conclusions: This study opens a research path that can promote the use of RNNs and PSSM profiles in bioinformatics and computational biology. Our approach is reproducible by scientists that aim to improve the performance results of different protein function prediction problems. Our source code and datasets are available at https://github.com/ngphubinh/adaptors.

scholarly journals Classification of adaptor proteins using recurrent neural networks and PSSM profiles

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Nguyen Quoc Khanh Le ◽  
Quang H. Nguyen ◽  
Xuan Chen ◽  
Susanto Rahardja ◽  
Binh P. Nguyen
Keyword(s):  
Neural Networks ◽  
Computational Biology ◽  
Recurrent Neural Networks ◽  
Protein Function ◽  
Intracellular Signaling ◽  
Protein Function Prediction ◽  
Characteristic Curve ◽  
Adaptor Proteins ◽  
The Neural Networks ◽  
Prediction Problems

Abstract Background Adaptor proteins are carrier proteins that play a crucial role in signal transduction. They commonly consist of several modular domains, each having its own binding activity and operating by forming complexes with other intracellular-signaling molecules. Many studies determined that the adaptor proteins had been implicated in a variety of human diseases. Therefore, creating a precise model to predict the function of adaptor proteins is one of the vital tasks in bioinformatics and computational biology. Few computational biology studies have been conducted to predict the protein functions, and in most of those studies, position specific scoring matrix (PSSM) profiles had been used as the features to be fed into the neural networks. However, the neural networks could not reach the optimal result because the sequential information in PSSMs has been lost. This study proposes an innovative approach by incorporating recurrent neural networks (RNNs) and PSSM profiles to resolve this problem. Results Compared to other state-of-the-art methods which had been applied successfully in other problems, our method achieves enhancement in all of the common measurement metrics. The area under the receiver operating characteristic curve (AUC) metric in prediction of adaptor proteins in the cross-validation and independent datasets are 0.893 and 0.853, respectively. Conclusions This study opens a research path that can promote the use of RNNs and PSSM profiles in bioinformatics and computational biology. Our approach is reproducible by scientists that aim to improve the performance results of different protein function prediction problems. Our source code and datasets are available at https://github.com/ngphubinh/adaptors.

scholarly journals Classification of adaptor proteins using recurrent neural networks and PSSM profiles

2021 ◽  
Author(s):  
NQ Khanh Le ◽  
QH Nguyen ◽  
X Chen ◽  
S Rahardja ◽  
Binh Nguyen
Keyword(s):  
Neural Networks ◽  
Computational Biology ◽  
Recurrent Neural Networks ◽  
Protein Function ◽  
Intracellular Signaling ◽  
Protein Function Prediction ◽  
Characteristic Curve ◽  
Adaptor Proteins ◽  
The Neural Networks ◽  
Prediction Problems

© 2019 The Author(s). Background: Adaptor proteins are carrier proteins that play a crucial role in signal transduction. They commonly consist of several modular domains, each having its own binding activity and operating by forming complexes with other intracellular-signaling molecules. Many studies determined that the adaptor proteins had been implicated in a variety of human diseases. Therefore, creating a precise model to predict the function of adaptor proteins is one of the vital tasks in bioinformatics and computational biology. Few computational biology studies have been conducted to predict the protein functions, and in most of those studies, position specific scoring matrix (PSSM) profiles had been used as the features to be fed into the neural networks. However, the neural networks could not reach the optimal result because the sequential information in PSSMs has been lost. This study proposes an innovative approach by incorporating recurrent neural networks (RNNs) and PSSM profiles to resolve this problem. Results: Compared to other state-of-the-art methods which had been applied successfully in other problems, our method achieves enhancement in all of the common measurement metrics. The area under the receiver operating characteristic curve (AUC) metric in prediction of adaptor proteins in the cross-validation and independent datasets are 0.893 and 0.853, respectively. Conclusions: This study opens a research path that can promote the use of RNNs and PSSM profiles in bioinformatics and computational biology. Our approach is reproducible by scientists that aim to improve the performance results of different protein function prediction problems. Our source code and datasets are available at https://github.com/ngphubinh/adaptors.

Automate Labeling Of Bugs and Tickets Using Attention-Based Mechanism in Recurrent Neral Networks

2021 ◽  
Vol 9 (11) ◽  
pp. 1411-1418
Author(s):  
Ravi Kauthale
Keyword(s):  
Neural Networks ◽  
Text Classification ◽  
Recurrent Neural Networks ◽  
Support Systems ◽  
The Neural Networks

Abstract: The aim here is to explore the methods to automate the labelling of the information that is present in bug trackers and client support systems. This is majorly based on the classification of the content depending on some criteria e.g., priority or product area. Labelling of the tickets is important as it helps in effective and efficient handling of the ticket and help is quicker and comprehensive resolution of the tickets. The main goal of the project is to analyze the existing methodologies used for automated labelling and then use a newer approach and compare the results. The existing methodologies are the ones which are based of the neural networks and without neural networks. In this project, a newer approach based on the recurrent neural networks which are based on the hierarchical attention paradigm will be used. Keywords: Automate Labeling, Recurrent Neural Networks, Hierarchical Attention, Multi-class Text Classification, GRU

Analysis and Design of Associative Memories Based on Recurrent Neural Networks with Linear Saturation Activation Functions and Time-Varying Delays

2007 ◽  
Vol 19 (8) ◽  
pp. 2149-2182 ◽  
Author(s):  
Zhigang Zeng ◽  
Jun Wang
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Sufficient Conditions ◽  
Time Varying ◽  
Activation Functions ◽  
Analysis And Design ◽  
Design Procedures ◽  
The Neural Network ◽  
Saturation Region ◽  
The Neural Networks

In this letter, some sufficient conditions are obtained to guarantee recurrent neural networks with linear saturation activation functions, and time-varying delays have multiequilibria located in the saturation region and the boundaries of the saturation region. These results on pattern characterization are used to analyze and design autoassociative memories, which are directly based on the parameters of the neural networks. Moreover, a formula for the numbers of spurious equilibria is also derived. Four design procedures for recurrent neural networks with linear saturation activation functions and time-varying delays are developed based on stability results. Two of these procedures allow the neural network to be capable of learning and forgetting. Finally, simulation results demonstrate the validity and characteristics of the proposed approach.

CONVERGENCE OF DISCRETE-TIME RECURRENT NEURAL NETWORKS WITH VARIABLE DELAY

2005 ◽  
Vol 15 (02) ◽  
pp. 581-595 ◽  
Author(s):  
JINLING LIANG ◽  
JINDE CAO ◽  
JAMES LAM
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Discrete Time ◽  
Recurrent Neural Networks ◽  
Matrix Decomposition ◽  
Linear Matrix ◽  
Variable Delay ◽  
Stability Criteria ◽  
Lmi Approach ◽  
The Neural Networks

In this paper, some global exponential stability criteria for the equilibrium point of discrete-time recurrent neural networks with variable delay are presented by using the linear matrix inequality (LMI) approach. The neural networks considered are assumed to have asymmetric weighting matrices throughout this paper. On the other hand, by applying matrix decomposition, the model is embedded into a cooperative one, the latter possesses important order-preserving properties which are basic to our analysis. A sufficient condition is obtained ensuring the componentwise exponential stability of the system with specific performances such as decay rate and trajectory bounds.

scholarly journals Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients

2021 ◽  
Vol 11 (9) ◽  
pp. 934
Author(s):  
Thanakron Na Pattalung ◽  
Thammasin Ingviya ◽  
Sitthichok Chaichulee
Keyword(s):  
Neural Networks ◽  
Critical Care ◽  
Intensive Care ◽  
Recurrent Neural Networks ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Care Staff ◽  
Intensive Care Patients ◽  
Clinical Observations ◽  
Risk Of Mortality

Critical care staff are presented with a large amount of data, which made it difficult to systematically evaluate. Early detection of patients whose condition is deteriorating could reduce mortality, improve treatment outcomes, and allow a better use of healthcare resources. In this study, we propose a data-driven framework for predicting the risk of mortality that combines high-accuracy recurrent neural networks with interpretable explanations. Our model processes time-series of vital signs and laboratory observations to predict the probability of a patient’s mortality in the intensive care unit (ICU). We investigated our approach on three public critical care databases: Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III), MIMIC-IV, and eICU. Our models achieved an area under the receiver operating characteristic curve (AUC) of 0.87–0.91. Our approach was not only able to provide the predicted mortality risk but also to recognize and explain the historical contributions of the associated factors to the prediction. The explanations provided by our model were consistent with the literature. Patients may benefit from early intervention if their clinical observations in the ICU are continuously monitored in real time.

scholarly journals Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks

2018 ◽  
Author(s):  
Cen Wan ◽  
Domenico Cozzetto ◽  
Rui Fa ◽  
David T. Jones
Keyword(s):  
Neural Networks ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Network Embedding ◽  
Protein Protein Interactions ◽  
Functional Representations

Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other network embedding-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.

Chaotic Dynamics of Discrete Multiple-Time Delayed Neural Networks of Ring Architecture Evoked by External Inputs

2016 ◽  
Vol 26 (11) ◽  
pp. 1650179 ◽  
Author(s):  
Xiaoying Wu ◽  
Yuanlong Chen ◽  
Jing Tian ◽  
Liangliang Li
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Chaotic Dynamics ◽  
Invariant Set ◽  
Multiple Time ◽  
Neuron Network ◽  
Delayed Neural Networks ◽  
The Neural Networks ◽  
Full Shift ◽  
Ring Architecture

In this paper, we consider a general class of discrete multiple-time delayed recurrent neural networks with external inputs. By applying a new transformation, we transform an m-neuron network model into a parameterized map from [Formula: see text] to [Formula: see text]. A chaotic invariant set of the neural networks system is obtained by using a family of projections from [Formula: see text] onto [Formula: see text]. Furthermore, we prove that the dynamics of this neural networks system restricted to the chaotic invariant set is topologically conjugate to the dynamics of the full shift map with two symbols, which indicates that chaos occurs. Numerical simulations are presented to illustrate the theoretical outcomes.

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