scholarly journals A Computational Approach for the Identification of Small GTPases Based on Preprocessed Amino Acid Sequences

2009 ◽  
Vol 8 (5) ◽  
pp. 333-341 ◽  
Author(s):  
Dominik Heider ◽  
Jessica Appelmann ◽  
Tuygun Bayro ◽  
Winfried Dreckmann ◽  
Andreas Held ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Secondary Structure ◽  
Prediction Accuracy ◽  
Biological Activities ◽  
Small Gtpases ◽  
Computational Approach ◽  
Small Gtpase ◽  
Amino Acid Sequences ◽  
Network Cluster

The prediction of essential biological features based on a given protein sequence is a challenging task in computational biology. To limit the amount of in vitro verification, the prediction of essential biological activities gives the opportunity to detect so far unknown sequences with similar properties. Besides the application within the identification of proteins being involved in tumorigenesis, other functional classes of proteins can be predicted. The prediction accuracy depends on the selected machine learning approach and even more on the composition of the descriptor set used. A computational approach based on feedforward neural networks was applied for the prediction of small GTPases. Consequently, this was realized by taking secondary structure and hydrophobicity information as a preprocessing architecture and thus, as descriptors for the neural networks. We developed a neural network cluster, which consists of a filter network and four subfamily networks. The filter network was trained to identify small GTPases and the subfamily networks were trained to assign a small GTPase to one of the subfamilies. The accuracy of the prediction, whether a given sequence represents a small GTPase is very high (98.25%). The classifications of the subfamily networks yield comparable accuracy. The high prediction accuracy of the neural network cluster developed, gives the opportunity to suggest the use of hydrophobicity and secondary structure prediction in combination with a neural network cluster, as a promising method for the prediction of essential biological activities.

scholarly journals Fast learning neural network based on texture for Arabic calligraphy identification

2021 ◽  
Vol 21 (3) ◽  
pp. 1794
Author(s):  
Ahmed Kawther Hussein
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Prediction Accuracy ◽  
Input Output ◽  
Fast Learning ◽  
Input And Output ◽  
Learning Machine ◽  
Hidden Layer ◽  
Arabic Calligraphy

<span id="docs-internal-guid-5c723154-7fff-a7b2-3582-b7c2920a9921"><span>Arabic calligraphy is considered a sort of Arabic writing art where letters in Arabic can be written in various curvy or segments styles. The efforts of automating the identification of Arabic calligraphy by using artificial intelligence were less comparing with other languages. Hence, this article proposes using four types of features and a single hidden layer neural network for training on Arabic calligraphy and predicting the type of calligraphy that is used. For neural networks, we compared the case of non-connected input and output layers in extreme learning machine ELM and the case of connected input-output layers in FLN. The prediction accuracy of fast learning machine FLN was superior comparing ELM that showed a variation in the obtained accuracy. </span></span>

A Prediction Method about Central Heating Parameters Based on BP Neural Networks

2012 ◽  
Vol 512-515 ◽  
pp. 2721-2725
Author(s):  
Bao Liang Sha ◽  
Zhen Zhang ◽  
Bing Kun Gao ◽  
Ming Liu ◽  
Shu Fang Yuan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Water Temperature ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Outdoor Temperature ◽  
Central Heating ◽  
System Flow ◽  
Saving Effect

First, combined with the theory of heating operation adjustment, the advising heat is determined; Then, BP neural network is used to seek the mapping relationship among the outdoor temperature, system flow, supplying and returning water temperature, etc. This method achieved higher prediction accuracy, and energy-saving effect is remarkable.

Protein Structure Prediction by Fusion,Bayesian Methods

2011 ◽  
pp. 1330-1336
Author(s):  
Somasheker Akkaladevi ◽  
Ajay K. Katangur ◽  
Xin Luo
Keyword(s):  
Neural Network ◽  
Amino Acids ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Prediction Accuracy ◽  
Protein Secondary Structure ◽  
Decision Fusion ◽  
Amino Acid Residues ◽  
Fusion Methods ◽  
Bayesian Inference Method

Prediction of protein secondary structure (alpha-helix, beta-sheet, coil) from primary sequence of amino acids is a very challenging and difficult task, and the problem has been approached from several angles. A protein is a sequence of amino acid residues and can thus be considered as a one dimensional chain of ‘beads’ where each bead correspond to one of the 20 different amino acid residues known to occur in proteins. The length of most protein sequence ranges from 50 residues to about 1000 residues but longer proteins are also known, e.g. myosin, the major protein of muscle fibers, consists of 1800 residues (Altschul et al. 1997). Many techniques were used many researchers to predict the protein secondary structure, but the most commonly used technique for protein secondary structure prediction is the neural network (Qian et al. 1988). This chapter discusses a new method combining profile-based neural networks (Rost et al. 1993b), Simulated Annealing (SA) (Akkaladevi et al. 2005; Simons et al. 1997), Genetic algorithm (GA) (Akkaladevi et al. 2005) and the decision fusion algorithms (Akkaladevi et al. 2005). Researchers used the neural network (Hopfield 1982) combined with GA and SA algorithms, and then applied the two decision fusion methods; committee method and the correlation methods and obtained improved results on the prediction accuracy (Akkaladevi et al. 2005). Sequence profiles of amino acids are fed as input to the profile-based neural network. The two decision fusion methods improved the prediction accuracy, but noticeably one method worked better in some cases and the other method for some other sequence profiles of amino acids as input (Akkaladevi et al. 2005). Instead of compromising on some of the good solutions that could have generated from either approach, a combination of these two approaches is used for obtaining better prediction accuracy. This criterion is the basis for the Bayesian inference method (Anandalingam et al. 1989; Schmidler et al. 2000; Simons et al. 1997). The results obtained show that the prediction accuracy improves by more than 2% using the combination of the decision fusion approach and the Bayesian inference method.

Travel Mode Choice Modeling: A Comparison of Bayesian Networks and Neural Networks

2012 ◽  
Vol 209-211 ◽  
pp. 717-723
Author(s):  
Dou Nan Tang ◽  
Min Yang ◽  
Mei Hui Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bayesian Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Prediction Performance ◽  
Bayesian Network Model ◽  
Travel Mode ◽  
Travel Mode Choice

In recent years, Bayesian networks and neural networks have been widely applied to the travel demand prediction area. However, their prediction performance is rarely directly compared. By experimental tests conducted using the same dataset, a Bayesian network model and a neural network model are compared for the travel mode analysis for the first time in this paper. It is found that the fully Bayesian network model tends to overfit the training set when the network itself is considerable complicated. The TAN structure otherwise has a better generalization performance and can achieve a better and more stable prediction performance, for its prediction accuracy 75.4%±0.63%, compared to the BP neural network model ,which prediction accuracy is 72.2%±3.01%. Experiment and statistical tests demonstrate the superiority of Bayesian networks and we propose using Bayesian networks, especially TAN, instead of neural networks in the travel mode choice prediction field.

scholarly journals Convolutional Neural Network Based Approach to Detect Pedestrians in Real-Time videos

2020 ◽  
Vol 10 (1) ◽  
pp. 303-308
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Prediction Accuracy ◽  
Video Quality ◽  
Test Set ◽  
High Prediction ◽  
The Given

Pedestrians in the vehicle way are in peril of being hit, along these lines making extreme damage walkers and vehicle inhabitants. Hence, constant person on foot identification was done through a set of recorded videos and the system detects the persons/pedestrians in the given input videos. In this survey, a continuous plan was proposed dependent on Aggregated Channel Features (ACF) and CPU. The proposed technique doesn't have to resize the information picture neither the video quality. We also use SVM with HOG and SVM with HAAR to detect the pedestrians. In addition, the Convolutional Neural Networks (CNN) were trained with a set of pedestrian images datasets and later tested on some test-set of pedestrian images. The analyses demonstrated that the proposed technique could be utilized to distinguish people on foot in the video with satisfactory mistake rates and high prediction accuracy. In this manner, it tends to be applied progressively for any real-time streaming of videos and also for prediction of pedestrians in prerecorded videos.

Predicting Protein Secondary Structure Using Artificial Neural Networks and Information Theory

2011 ◽  
pp. 335-360
Author(s):  
Saad O.A. Subair ◽  
Safaai Deris
Keyword(s):  
Neural Networks ◽  
Information Theory ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
3D Structure ◽  
Protein Secondary Structure ◽  
Amino Acid Sequences ◽  
Prediction Methods ◽  
Hardware Platforms

Protein secondary-structure prediction is a fundamental step in determining the 3D structure of a protein. In this chapter, a new method for predicting protein secondary structure from amino-acid sequences has been proposed and implemented. Cuff and Barton 513 protein data set is used in training and testing the prediction methods under the same hardware, platforms, and environments. The newly developed method utilizes the knowledge of the GOR-V information theory and the power of the neural networks to classify a novel protein sequence in one of its three secondary-structures classes (i.e., helices, strands, and coils). The newly developed method (NN-GORV-I) is further improved by applying a filtering mechanism to the searched database and hence named NN-GORV-II. The developed prediction methods are rigorously analyzed and tested together with the other five well-known prediction methods in this domain to allow easy comparison and clear conclusions.

General Framework for Two-stage Approaches to Protein Secondary Structure Prediction

2003 ◽  
Vol 07 (03) ◽  
pp. 122-128
Author(s):  
Jagath C. Rajapakse ◽  
Minh N. Aguyen
Keyword(s):  
Neural Network ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Amino Acid Sequences ◽  
Single Stage ◽  
Support Vector ◽  
Protein Secondary Structure Prediction ◽  
Two Stage

Bioinformatics techniques to protein secondary structure prediction, such as Support Vector Machine (SVM) and GOR approaches, are mostly single-stage approaches; they predict secondary structures of the protein by taking into account only the information available in amino acid sequences. On the other hand, PHD (Profile network from HeiDelberg) method is a two-stage technique where two Multi-Layer Perceptrons (MLPs) are cascaded; the second neural network receives the output of the first neural network captures any contextual relationships among the secondary structure elements predicted by the first neural network. In this paper, we argue that it is feasible to extend the current single-stage approaches by adding a second-stage prediction scheme to capture the contextual information among secondary structural elements and thereby improving their accuracies. We demonstrate that two-stage SVMs perform better than present techniques for protein secondary structure prediction.

AIR POLLUTANTS CONCENTRATION PREDICTION BASED ON TRANSFER LEARNING AND RECURRENT NEURAL NETWORK

2020 ◽  
Vol 2 (1) ◽  
pp. 103-115
Author(s):  
Fong Iat Hang ◽  
Simon Fong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Air Pollution ◽  
Human Health ◽  
Transfer Learning ◽  
Recurrent Neural Networks ◽  
Air Pollutants ◽  
Prediction Accuracy ◽  
Meteorological Data ◽  
Concentration Of Air Pollutants

Air pollution poses a great threat to human health, and people are paying more and more attention to the prediction of air pollution. Prediction of air pollution helps people plan for their outdoor activities and helps protect human health. In this article, long-short term memory recurrent neural networks were used to predict the future concentration of air pollutants in Macau. In addition, meteorological data and data on the concentration of air pollutants were used. Moreover, in Macau, some air quality monitoring stations have less observed data, and some AQMSs less observed data of certain types of air pollutants. Therefore, the transfer learning and pre-trained neural networks were used to assist AQMSs with less observed data to generate neural network with high prediction accuracy. In this thesis, in most cases, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy, used less training time than randomly initialized recurrent neural networks.

Sign in / Sign up

Export Citation Format

Share Document