6,686 Hits in 4.2 sec

Artificial Neural Network aided Protein Structure Prediction

Arundhati Deka, Kandarpa Kr. Sarma
2012 International Journal of Computer Applications  
But these conventional methods are now replaced by Machine learning methods such as Artificial Neural Network (ANN) and Support Vector Machine (SVM)s.  ...  In this paper, ANNs are used as a two level classifier to estimate the tertiary structure of proteins.  ...  BASICS OF ANN Artificial neural network (ANN) is made up of interconnecting artificial neurons.  ... 
doi:10.5120/7450-0494 fatcat:wp6g4aisdnfnvcqoeb2lcjmaxa

Predicting the Secondary Structure of Proteins Using Artificial Neural Networks

Betul Akcesme, Faruk Berat Akcesme
2014 Southeast Europe Journal of Soft Computing  
A method for protein secondary structure prediction based on the use of artificial neural networks (ANN) is presented.  ...  A neural network with only an input and an output layer is used, and backpropagation technique is adopted to tune the synaptic weights. Data is divided into two sets for training, and testing.  ...  Perceptrons As the base topology of artificial neural network (Tang et.  ... 
doi:10.21533/scjournal.v3i2.10 fatcat:lax2sqk3szew5p37tpygzmri5u

An hierarchical artificial neural network system for the classification of transmembrane proteins [article]

Claude Pasquier, Stavros Hamodrakas
2016 arXiv   pre-print
This work presents a simple artificial neural network which classifies proteins into two classes from their sequences alone: the membrane protein class and the non-membrane protein class.  ...  Applied to a test set of 995 globular, water-soluble proteins, the neural network classified falsely 23 of them in the membrane protein class (97.7% of correct assignment).  ...  The systems above are not using neural network system for the classification. We show here that a simple neural network systems can be applied to this kind of problem in a successful way.  ... 
arXiv:0902.3148v2 fatcat:jtilkyvofrglpln6wzmgw2hi3q

Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics

Aida Catic, Lejla Gurbeta, Amina Kurtovic-Kozaric, Senad Mehmedbasic, Almir Badnjevic
2018 BMC Medical Genomics  
The usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years.  ...  Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman's) neural network was 99.00%.  ...  Acknowledgements The authors would like to thank the editor and reviewers for the thoughtful comments and constructive suggestions, which greatly helped us improve the quality of this manuscript.  ... 
doi:10.1186/s12920-018-0333-2 pmid:29439729 pmcid:PMC5812210 fatcat:e7ehspskf5efdmcq2qbvihfk4e

An hierarchical artificial neural network system for the classification of transmembrane proteins

C. Pasquier, S.J. Hamodrakas
1999 Protein Engineering Design & Selection  
The systems above are not using neural network system for the classification. We show here that a simple neural network systems can be applied to this kind of problem in a successful way.  ...  Now, we have extended this application with a pre-processing stage represented by an artificial neural network which attempts to classify proteins to membrane and non-membrane.  ... 
doi:10.1093/protein/12.8.631 pmid:10469822 fatcat:hfturmwdl5bc7cifnwh6dndzne

Bioinformatic analyses of mammalian 5'-UTR sequence properties of mRNAs predicts alternative translation initiation sites

Jill L Wegrzyn, Thomas M Drudge, Faramarz Valafar, Vivian Hook
2008 BMC Bioinformatics  
Critical parameters of 5'-UTRs were assessed by an Artificial Neural Network (ANN) for identification of the aTIS codon and its location.  ...  ANNs have previously been used for the purpose of AUG start site prediction and are applicable in complex.  ...  Training Artificial Neural Network (ANN) for aTIS Identification Artificial Neural Networks have been successfully used in the automated location of AUG translation initiation sites [41, 53] .  ... 
doi:10.1186/1471-2105-9-232 pmid:18466625 pmcid:PMC2396638 fatcat:yk5yob5v7bggzizsdn3nnb5eca

Strand-loop-strand motifs: Prediction of hairpins and diverging turns in proteins

Michael Kuhn, Jens Meiler, David Baker
2003 Proteins: Structure, Function, and Bioinformatics  
The neural network is trained with a representative subset of the Protein Data Bank and achieves a prediction accuracy of 75.9 ؎ 4.4% compared to a baseline prediction rate of 59.1%.  ...  The ␤-hairpin/diverging turn classification from amino acid sequences is available online for academic use (Meiler and Kuhn, 2003; html). Proteins 2004;54:282-288.  ...  By using a scoring scheme and an artificial neural network, the number of strong matches is calculated.  ... 
doi:10.1002/prot.10589 pmid:14696190 fatcat:xmxxdnaznzb7fn4itek2fqiubi

Automatic derivation of substructures yields novel structural building blocks in globular proteins

X Zhang, J S Fetrow, W A Rennie, D L Waltz, G Berg
1993 Proceedings. International Conference on Intelligent Systems for Molecular Biology  
This work presents the results of an alternative approach where local structure classes in proteins are derived using neural network and clustering techniques.  ...  , of proteins.  ...  Here we present the results of a categorization system combining artificial neural network and clustering techniques.  ... 
pmid:7584368 fatcat:hte2sdkd3zfq5mnlwqqkmeky6m

Protein Secondary Structure Prediction using Deep Neural Network and Particle Swarm Optimization Algorithm

Angela U., Adetayo Sylvester
2018 International Journal of Computer Applications  
The basic particle swarm optimization algorithm was used in training a deep neural network as implemented using Java programming language with spring boot framework for generating the various APIs.  ...  In this work, deep neural network with three (3) hidden layers and particle swarm optimization algorithms are combined to predict the secondary structure of proteins from their primary structures (Amino  ...  Backward propagation is a supervised learning algorithm of artificial neural networks using gradient descent.  ... 
doi:10.5120/ijca2018918070 fatcat:6x2iuygd2jenndnkw5wtv7oe7y

ZPRED: Predicting the distance to the membrane center for residues in -helical membrane proteins

E. Granseth, H. Viklund, A. Elofsson
2006 Bioinformatics  
Results: We show that the Z-coordinate can be predicted using either artificial neural networks, hidden Markov models or combinations of both.  ...  The best method, ZPRED, uses the output from a hidden Markov model together with a neural network.  ...  The prediction can be performed using either an artificial neural network or a hidden Markov model with roughly the same error rate.  ... 
doi:10.1093/bioinformatics/btl206 pmid:16873471 fatcat:hx3mvsmmnrdbxpo7raqq57wcyy

Deep Learning Approaches for Protein Structure Prediction

Khatri Chandni, Prof. Mrudang Pandya, Dr. Sunil Jardosh
2018 International Journal of Engineering & Technology  
In recent years, Machine Learning techniques that are based on Deep Learning networks that show a great promise in research communities.Successful methods for deep learning involve Artificial Neural Networks  ...  In these paper aimed to review work based on protein structure prediction solve using Deep Learning Networks.  ...  Deep neural network(DNN) is an artificial neural networks having multiple layers of units between input and output layers [6] .  ... 
doi:10.14419/ijet.v7i4.5.20037 fatcat:wppywuzward57nrltevfc6rfsm

Deep learning in bioinformatics

Wei Wang, Xin Gao
2019 Methods  
The foundation of most modern deep learning models is artificial neural networks.  ...  The most successful architectures are convolutional neural networks (CNNs) and recurrent neural network (RNN), which are now the cornerstone of all leading methods in image classification and natural language  ...  It then introduces deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative  ... 
doi:10.1016/j.ymeth.2019.06.006 pmid:31181259 fatcat:n4hy4vim6jcl5o2uvxneqnli7m

A Genetic Programming Method for the Identification of Signal Peptides and Prediction of Their Cleavage Sites

David Lennartsson, Peter Nordin
2004 EURASIP Journal on Advances in Signal Processing  
The method is compared with a previous work using artificial neural network (ANN) approaches. Some advantages compared to ANNs are noted.  ...  We use an evolutionary algorithm for automatic evolution of classification programs, so-called programmatic motifs.  ...  The use of genetic programming (GP) for protein classification tasks has been pioneered by Koza.  ... 
doi:10.1155/s1110865704309108 fatcat:j6lhlxzhrfgw7ml6lubaqszwtm

Predicting protein secondary structure based on ensemble Neural Network

Emmanuel Gbenga Dada, David Opeoluwa Oyewola, Joseph Hurcha Yakubu, Ayotunde Alaba Fadele
2021 ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)  
We proposed an Ensemble Neural Network learning model that consists of some Neural Network algorithms such as Feed Forward Neural Network (FFNN), Recurrent Neural Network (RNN), Cascade Forward Network  ...  The reasons for these are due to the vague dissimilar sequences among protein structures, meaningless protein data, high dimensional data, and having to deal with highly imbalanced classification task.  ...  secondary structures.  We used four (4) different neural network classifiers and jointly combined the classification results to represent the final results.  Better classification accuracy of protein  ... 
doi:10.5935/jetia.v7i27.732 fatcat:unneqh4f75eephjezht4rbljt4

Deep Neural Network Based Precursor microRNA Prediction on Eleven Species [article]

Jaya Thomas, Lee Sael
2017 arXiv   pre-print
The deep neural network based classification outperformed support vector machine, neural network, naive Baye's classifiers, k-nearest neighbors, random forests, and a hybrid system combining support vector  ...  The feature set based Restricted Boltzmann Machine method, which we call DP-miRNA, uses 58 features that are categorized into four groups: sequence features, folding measures, stem-loop features and statistical  ...  Many tools have been developed based on the different classification techniques such as naive Bayes classifier (NBC), artificial neural networks (ANN), support vector machines (SVM), and random forests  ... 
arXiv:1704.03834v1 fatcat:uzs2pkfcavhltj5h2y3qxyz3ua
« Previous Showing results 1 — 15 out of 6,686 results