Protein secondary structure and homology by neural networks The α‐helices in rhodopsin

Bohr, Henrik; Bohr, Jakob; Brunak, Søren; Cotterill, R. M. J.; Lautrup, B.; Nørskov, Leif; Olsen, Ole H.; Petersen, Steffen B.

doi:10.1016/0014-5793(88)81066-4

Cited by 168 publications

(42 citation statements)

References 29 publications

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“…Some of the computational methods that are used to achieve secondary structure predictions include Artificial Neural Networks(ANN) [20] [109]. A review of literature on computational techniques for secondary structure prediction using neural network indicates that multilayer feed forward neural networks are the most preferred and effective tool [111][118] [19].…”

Section: Predictionmentioning

confidence: 99%

Reaching optimized parameter set: protein secondary structure prediction using neural network

Dongardive

Abraham

2016

Neural Comput & Applic

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Abstract:We propose an optimized parameter set for protein secondary structure prediction using three layer feed forward back propagation neural network. The methodology uses four parameters viz. encoding scheme, window size, number of neurons in the hidden layer and type of learning algorithm. The input layer of the network consists of neurons changing from 3 to 19, corresponding to different window sizes. The hidden layer chooses a natural number from 1 to 20 as the number of neurons. The output layer consists of three neurons, each corresponding to known secondary structural classes viz. α -helix, β-strands and coil/turns respectively. It also uses eight different learning algorithms and nine encoding schemes. Exhaustive experiments were performed using non-homologues dataset. The experimental results were compared using performance measures like Q 3 , sensitivity, specificity, Mathew correlation coefficient and accuracy. The paper also discusses the process of obtaining a stabilized cluster of 2530 records from a collection of 11340 records. The graphs of these stabilized clusters of records with respect to accuracy are concave, convergence is monotonic increasing and rate of convergence is uniform. The paper gives BLOSUM62 as the encoding scheme, 19 as the window size, 19 as the number of neurons in the hidden layer and One-Step Secant as the learning algorithm with the highest accuracy of 78%. These parameter values are proposed as the optimized parameter set for the three layer feed forward back propagation neural network for the protein secondary structure prediction.

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Section: Predictionmentioning

confidence: 99%

Reaching optimized parameter set: protein secondary structure prediction using neural network

Dongardive

Abraham

2016

Neural Comput & Applic

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“…In the same year, two studies employing ANNs were published with the aim to predict the secondary structure of proteins [42,43].…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

Applications of Artificial Neural Networks in Chemical Problems

Matarollo¹,

Honório²,

Silva³

2013

Artificial Neural Networks - Architectures and Applications

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“…Subsequent studies proposed some variants of this approach [21][22][23][24]26]. For example, in [23] the input of the neural network has been augmented with the hydrophobicity of each residue, while [25] studied different encoding schemes and a modular architecture.…”

Section: Protein Structure Predictionmentioning

confidence: 99%

Natural computing methods in bioinformatics: A survey

Masulli

2009

Information Fusion

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Often data analysis problems in Bioinformatics concern the fusion of multisensor outputs or the fusion of multi-source information, where one must integrate different kinds of biological data. Natural computing provides several possibilities in Bioinformatics, especially by presenting interesting nature-inspired methodologies for handling such complex problems. In this article we survey the role of natural computing in the domains of protein structure prediction, microarray data analysis and gene regulatory network generation. We utilize the learning ability of neural networks for adapting, uncertainty handling capacity of fuzzy sets and rough sets for modeling ambiguity, and the search potential of genetic algorithms for efficiently traversing large search spaces.

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Protein secondary structure and homology by neural networks The α‐helices in rhodopsin

Cited by 168 publications

References 29 publications

Reaching optimized parameter set: protein secondary structure prediction using neural network

Reaching optimized parameter set: protein secondary structure prediction using neural network

Applications of Artificial Neural Networks in Chemical Problems

Natural computing methods in bioinformatics: A survey

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