Hybrid system for protein secondary structure prediction

Zhang, Xiru; Mesirov, Jill P.; Waltz, David L.

doi:10.1016/0022-2836(92)90104-r

Cited by 185 publications

(84 citation statements)

References 15 publications

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“…In this case increasing hidden layer can improve the prediction result. we make comparison with another paper which the prediction result is 64% [10]. In addition, in this paper we calculate the output of prediction for 7 types of secondary structure.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, in this paper we calculate the output of prediction for 7 types of secondary structure. Meanwhile in another paper [3,9,10] use 3 types, there are Helix, Beta strand and Coil. Althought prediction of 7 types of secondary structure is more difficult, but the ANN still can predict for this problem.…”

Section: Resultsmentioning

confidence: 99%

“…In this problem, we use ANN as classification and recognition methods. Some calculation using ANN for Secondary structure prediction can be found in some papers [1,2,3,4,5,6,7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Method of Classification and Recognition of Blue Copper Protein

Purqon¹,

Sugiyama²,

Takamatsu³

et al. 2006

AIP Conference Proceedings

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Abstract. Some proteins in blue copper proteins have similar properties. In some cases it is not easy to distinguish the proteins each other. The study to recognize and classify in blue copper proteins has important roles to recognize the difference of similar properties, for examples, structures and residue sequences in blue copper proteins. There are many methods being developed to predict protein structure from many approachs, which one still not satisfactory yet. Therefore it is a challenge for scientists to develop or improve their methods. One of promising method is artificial neural networks (ANN). ANN is learning machine methods consisted of input, hidden and output layer. ANN is tested to recognize secondary structure in blue copper protein. It is found that ANN can distinguish for 7-type of secondary structure and recognize 72% secondary structure in blue copper protein.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Method of Classification and Recognition of Blue Copper Protein

Purqon¹,

Sugiyama²,

Takamatsu³

et al. 2006

AIP Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…Analogy based approaches are referred to as the nearestneighbor algorithm (Levin et al, 1986;Nishikawa and Ooi, 1986;Salzberg and Cost, 1992;Zhang et al, 1992;Geourjon and Deleage, 1994;Yi and Lander, 1994;Salamov and Solovyev, 1995). The core of all these procedures is the definition of similarity, just as in the fold recognition methods.…”

Section: Methodsmentioning

confidence: 99%

A Promising Similarity Based Secondary Structure Prediction Method

Akcesme

Can

2016

scjournal

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“…This is a 3-class classification task which consists in assigning a conformation α-helix, β-strand or coil, to each residue of a sequence. The classifiers used are the neural architecture and statistical model in [4], with the nearest-neighbours algorithm of [10]. We have compared our optimal solution to other combiners: a single hidden layer neural network (MLP), a logistic regression model and an optimal convex combination.…”

Section: Methodsmentioning

confidence: 99%