Knowledge base and neural network approach for protein secondary structure prediction

Patel, Maulika S.; Mazumdar, H. S.

doi:10.1016/j.jtbi.2014.08.005

Cited by 19 publications

(9 citation statements)

References 24 publications

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“…The methods have been extracted from literature based on PSSP and NN. [71,70] before 1990, [103,104,105,106,73,100,107] between 1990 and 1995, [108,109,110,98,111,78,76] between 1996 and 2000, [97,112,113,79,114,115,116,117,80] between 2001 and 2005, [118,119,96,120,121,122,123] between 2006 and 2010, [87,124,91,77,125,126,127,128,95,86] between 2011 and 2015, and [89,90,69,82] post 2015.…”

Section: Discussionmentioning

confidence: 99%

Protein secondary structure prediction using neural networks and deep learning: A review

Wardah

Khan

Sharma

et al. 2019

Computational Biology and Chemistry

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Section: Discussionmentioning

confidence: 99%

Protein secondary structure prediction using neural networks and deep learning: A review

Wardah

Khan

Sharma

et al. 2019

Computational Biology and Chemistry

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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%

“…Several methods reviewed in this section were developed in an ensemble or multi-component manner. 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.…”

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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“…They used two popular data sets RS126 and CB396 to evaluate the accuracy of the proposed model. The Q3 accuracy of 90.16% and 82.28% achieved on the RS126 and CB396 test sets respectively [11]. Yong Tat Tan and et al (2015) claimed that nearest neighbor -complexity distance measure (NN-CDM) algorithm using Lempel-Ziv (LZ) complexity-based distance measure had a problem.…”

Section: Related Workmentioning

confidence: 99%

RMSD Protein Tertiary Structure Prediction with Soft Computing

Iraji¹,

Ameri²

2016

IJMSC

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Root-mean-square-deviation (RMSD) is an indicator in protein-structure-prediction-algorithms (PSPAs). Goal of PSP algorithms is to obtain 0 Å RMSD from native protein structures. Protein structure and RMSD prediction is very essential. In 2013, the estimated RMSD proteins based on nine features were obtained best results using D2N (Distance to the native). We presented in This paper proposed approach to reduce predicted RMSD Error Than the actual amount for RMSD and calculate mean absolute error (MAE), through feed forward neural network, adaptive neuro fuzzy method. ANFIS is achieved better and more accurate results.

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Knowledge base and neural network approach for protein secondary structure prediction

Cited by 19 publications

References 24 publications

Protein secondary structure prediction using neural networks and deep learning: A review

Protein secondary structure prediction using neural networks and deep learning: A review

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

RMSD Protein Tertiary Structure Prediction with Soft Computing

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