Predicting protein secondary structure based on ensemble Neural Network
نویسندگان
چکیده
منابع مشابه
Artificial Neural Network Method for Predicting Protein Secondary Structure Content
In this paper, the neural network method was applied to predict the content of protein secondary structure elements that was based on 'pair-coupled amino acid composition', in which the sequence coupling effects are explicitly included through a series of conditional probability elements. The prediction was examined by a self-consistency test and an independent-dataset. Both indicated good resu...
متن کاملProtein Secondary Structure Prediction Based on Denoeux Belief Neural Network
Predicting the secondary structure of protein is an important step towards obtaining its three dimensional structure and consequently its function. At present, the best predictors are based on machine learning techniques, in particular neural network architectures. We introduce a new architecture called Denoeux belief neural network (DBNN) for the prediction problem. DBNN uses reference pattern...
متن کاملPredicting protein secondary structure using neural net and statistical methods.
A comparison of neural network methods and Bayesian statistical methods is presented for prediction of the secondary structure of proteins given their primary sequence. The Bayesian method makes the unphysical assumption that the probability of an amino acid occurring in each position in the protein is independent of the amino acids occurring elsewhere. However, we find the predictive accuracy ...
متن کاملPredicting protein secondary structure by cascade-correlation neural networks
The back-propagation neural network algorithm is a commonly used method for predicting the secondary structure of proteins. Whilst popular, this method can be slow to learn and here we compare it with an alternative: the cascade-correlation architecture. Using a constructive algorithm, cascade-correlation achieves predictive accuracies comparable to those obtained by back-propagation, in shorte...
متن کاملRule Learning based on Neural Network Ensemble
Neural network ensemble can significantly improve the generalization ability of neural network based systems. In this paper, a novel rule learning algorithm is proposed, where neural network ensemble acts as a front-end process that generates data for the learning of rules. Experimental results show that the proposed algorithm can generate rules with strong generalization ability.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)
سال: 2021
ISSN: 2447-0228
DOI: 10.5935/jetia.v7i27.732