A hybrid method to cluster protein sequences based on statistics and artificial neural networks

Ferràn, Edgardo A.; Pflugfelder, Bernard

doi:10.1093/bioinformatics/9.6.671

Cited by 12 publications

(11 citation statements)

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“…Although self-organized neural networks have already been used for the classification of proteins (Ferran et al 1994;Ferran and Pflugfelder 1993;Ferrara 1991, 1992;Andrade et al 1996) using sequence data, the approach presented here is completely different in the sense that a new type of self-organizing structure has been developed which grows according to the hypothetical pattern of speciation which would have given rise to the set of present-day sequences analyzed. Direct application of the Kohonen algorithm (Kohonen 1990) to data whose internal relationships are described by means of a binary tree may produce a correct segregation into the main groups but lacks a natural way to represent the taxonomic relationships among the individuals.…”

Section: Discussionmentioning

confidence: 99%

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Phylogenetic Reconstruction Using an Unsupervised Growing Neural Network That Adopts the Topology of a Phylogenetic Tree

Dopazo¹,

1997

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We propose a new type of unsupervised, growing, self-organizing neural network that expands itself by following the taxonomic relationships that exist among the sequences being classified. The binary tree topology of this neutral network, contrary to other more classical neural network topologies, permits an efficient classification of sequences. The growing nature of this procedure allows to stop it at the desired taxonomic level without the necessity of waiting until a complete phylogenetic tree is produced. This novel approach presents a number of other interesting properties, such as a time for convergence which is, approximately, a lineal function of the number of sequences. Computer simulation and a real example show that the algorithm accurately finds the phylogenetic tree that relates the data. All this makes the neural network presented here an excellent tool for phylogenetic analysis of a large number of sequences.

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Section: Discussionmentioning

confidence: 99%

“…In this study we will concentrate on a type of unsupervised neural network known as ''Kohonen self-organizing maps'' (Kohonen 1990). Previous works on sequence analysis have used this algorithm to classify protein sequences into groups (Ferran et al 1994;Ferran and Pflugfelder 1993;Ferrara 1991, 1992) based on their dipeptide compositions.…”

Section: Introductionmentioning

confidence: 99%

Phylogenetic Reconstruction Using an Unsupervised Growing Neural Network That Adopts the Topology of a Phylogenetic Tree

Dopazo¹,

1997

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“…In addition, we have recently shown that the results issued from the statistical methods can be used not only to validate the classifications obtained with the ANN approach, but also to choose, in a more reasonable way, the number of neurons of the network (Ferran & Pflugfelder, 1993). This can be done by relating a statistical determination of the optimal number of clusters with the number of neurons of the network.…”

Section: Discussionmentioning

confidence: 99%

Self‐organized neural maps of human protein sequences

Ferràn

Pflugfelder²,

Ferrara

1994

Protein Science

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We have recently described a method based on artificial neural networks to cluster protein sequences into families. The network was trained with Kohonen's unsupervised learning algorithm using, as inputs, the matrix patterns derived from the dipeptide composition of the proteins. We present here a large-scale application of that method to classify the 1,758 human protein sequences stored in the SwissProt database (release 19.0), whose lengths are greater than 50 amino acids. In the final 2-dimensional topologically ordered map of 15 X 15 neurons, proteins belonging to known families were associated with the same neuron or with neighboring ones. Also, as an attempt to reduce the time-consuming learning procedure, we compared 2 learning protocols: one of 500 epochs (100 SUN CPU-hours [CPU-h]), and another one of 30 epochs (6.7 CPU-h). A further reduction of learningcomputing time, by a factor of about 3.3, with similar protein clustering results, was achieved using a matrix of 11 x 11 components to represent the sequences. Although network training is time consuming, the classification of a new protein in the final ordered map is very fast (14.6 CPU-seconds). We also show a comparison between the artificial neural network approach and conventional methods of biosequence analysis.

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“…Some other examples of networks recognizing functional motifs were presented by Sternberg (1991, 1992); Ladunga et al (1991); Schneider and Wrede (1993); Hansen et al (1998);Nielsen et al (1997). The second approach is based on using the frequency with which any of the 20 * 20 possible amino acid pairs occurs in the sequence (Ferrán and Pflugfelder 1993), or on using the information extracted from database annotations (Andrade and Valencia 1997).…”

Section: Functional Predictionmentioning

confidence: 99%

“…For example, a neural network system for predicting various aspects of 1D structure based on evolutionary information is by far the most widely used prediction method (Rost et al 1994). Other network-based methods are unique, or superior in their field (Ferrán and Pflugfelder 1993;Riis and Krogh 1996;Andrade and Valencia 1997;Hansen et al 1998;Nielsen et al 1997). Furthermore, neural networks revealed data base errors, and principles underlying protein structures (Brunak 1991;Rost et al 1994;Tolstrup et al 1994;Blom et al 1996).…”

Section: Multiple Sequence Alignmentmentioning

confidence: 99%

Some operations research methods for analyzing protein sequences and structures

2009

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The operations research is probably one of the most successful field of applied mathematics used in economics, physics, chemistry, almost everywhere where one has to analyze huge amounts of data. Lately, these techniques of operations research were introduced in biology, especially in the protein analysis area to support biologists. The fast growth of protein data makes operations research an important issue in bioinformatics, a science which lays on the border between computer science and biology. This paper gives a short overview of the operations research techniques currently used to support structural and functional analysis of proteins.

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A hybrid method to cluster protein sequences based on statistics and artificial neural networks

Cited by 12 publications

References 0 publications

Phylogenetic Reconstruction Using an Unsupervised Growing Neural Network That Adopts the Topology of a Phylogenetic Tree

Phylogenetic Reconstruction Using an Unsupervised Growing Neural Network That Adopts the Topology of a Phylogenetic Tree

Self‐organized neural maps of human protein sequences

Some operations research methods for analyzing protein sequences and structures

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