Towards designing modular recurrent neural networks in learning protein secondary structures

Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

doi:10.1016/j.eswa.2011.12.059

Cited by 14 publications

(8 citation statements)

References 38 publications

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“…In reality, using an ingenious ANN can accurately predict the boundaries of a class. Experimental results of various research have shown that recurrent neural networks (RNN) [18][19][20] are very effective for processing protein sequence data. A variant of RNN called bidirectional recurrent neural networks is known to exploit the data of the complete structure.…”

Section: Related Workmentioning

confidence: 99%

Predicting protein secondary structure based on ensemble Neural Network

Dada

Oyewola

Yakubu

et al. 2021

ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)

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Protein structure prediction is very vital to innovative process of discovering new medications based on the knowledge of a biological target. It is also useful for scientifically exposing the biological basis of convoluted diseases and drug effects. Despite its usefulness, protein structure is very complex, thereby making its prediction to be arduous, timewasting and costly. These drawbacks necessitated the need to develop more effective techniques with high prediction capability. Conventional techniques for predicting protein structure are ineffective, perform poorly, expensive and slow. 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. 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 (CFN) and Non-linear Autoregressive Network with Exogenous (NARX) models. These models were trained using training algorithms such as Levenberg-Marquardt (LM), Resilient Back Propagation (RBP) and Scaled Conjugate Gradient (SCG) to improve the performance. Experimental results show that our proposed model has superior performance compared to the other models compared.

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Section: Related Workmentioning

confidence: 99%

Predicting protein secondary structure based on ensemble Neural Network

Dada

Oyewola

Yakubu

et al. 2021

ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)

View full text Add to dashboard Cite

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“…Neural networks and deep learning [2,4,5,12,40,44,49,50] Support vector machines (SVM) [32,39,55,56] Multi-component approaches [2,4,5,9,12,25,41,49,53] [ 7,18,39,44,50,[54][55][56] Probabilistic and mining methods: Primary probabilistic methods [11,14] are based on empirical analytics and mainly compute the tendency of each amino-acid in protein sequence to form a particular secondary structure (i.e. probabilities are calculated based on the frequency of each amino-acid in each secondary class).…”

Section: Naturalmentioning

confidence: 99%

“…The first group of multi-component approaches [9,12,41,49,53] employ complementary modules beside the learning algorithms to promote the prediction results. Similarly, aiming to foster prediction accuracy, the second category [2,4,5,25,39] exploit multiple classifiers of the same type with various features. The third class of Multi-component approaches [7,18,44,50,[54][55][56] combine classifiers of different types [1] [35] that are at times equipped with the complementary components.…”

Section: Naturalmentioning

confidence: 99%

MCP: A multi-component learning machine to predict protein secondary structure

Khalatbari

Kangavari

Hosseini

et al. 2019

Computers in Biology and Medicine

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The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightly connected to its specific 3D structure. Prediction of the protein secondary structure is a crucial intermediate step towards elucidating its 3D structure and function. Traditional experimental methods for prediction of protein structure are expensive and time-consuming. Therefore, various machine learning approaches have been proposed to predict the protein secondary structure. Nevertheless, the average accuracy of the suggested solutions has hardly reached beyond 80%. The possible underlying reasons are the ambiguous sequence-structure relation, noise in input protein data, class imbalance, and the high dimensionality of the encoding schemes that represent the protein sequence. In this paper, we propose an accurate multi-component prediction machine to overcome the challenges of protein structure prediction. We devise a multi-component designation to address the high complexity challenge in sequence-structure relation. Furthermore, we utilize a compound string dissimilarity measure to directly interpret protein sequence content and avoid information loss. In order to improve the accuracy, we employ two different classifiers including support vector machine and fuzzy nearest neighbor and collectively aggregate the classification outcomes to infer the final protein secondary structures. We conduct comprehensive experiments to compare our model with the current state-of-the-art approaches. The experimental results demonstrate that given a set of input sequences, our multi-component framework can accurately predict the protein structure. Nevertheless, the effectiveness of our unified model can be further enhanced through framework configuration.

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“…Such components have valuable applications, especially to vision and language datasets [13,14]. Deep NNs (DNN) have been applied successfully in speech recognition [15][16][17][18][19], voice conversion [20,21], bio-informatics [22][23][24], face recognition [11,25,26], object recognition [27,28], dimensionality reduction [10,[29][30][31][32], and modeling textures [33].…”

Section: Introductionmentioning

confidence: 99%

Towards designing modular recurrent neural networks in learning protein secondary structures

Cited by 14 publications

References 38 publications

Predicting protein secondary structure based on ensemble Neural Network

Predicting protein secondary structure based on ensemble Neural Network

MCP: A multi-component learning machine to predict protein secondary structure

A fast and efficient pre-training method based on layer-by-layer maximum discrimination for deep neural networks

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