Application of neural networks, PCA and feature extraction for prediction of nucleotide sequences by using genomic signals

Cristea, Paul Dan; Mladenov, Valeri; Tsenov, Georgi; Tuduce, Rodica; Petrakieva, Simona

doi:10.1109/neurel.2008.4685575

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2010

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“…Concerning [15] and [18] the input data is processed in two modules -an n-dimensional Fast Fourier Transform block, and an Artificial Neural Network block. The FFT block reduces the number of dimensions from n input samples to m uncorrelated non-zero Fourier coefficients in the frequency domain, used as inputs of the ANN.…”

Section: Interf Acementioning

confidence: 99%

Performance comparison of techniques for DNA sequence prediction using neural networks

Nikolova

Mladenov

Tsenov

2010

2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP)

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The conversion of symbolic sequences into complex genomic signals reveals surprising regularities of genomes, both locally and at a global scale. This approach allows usage of standard signal processing methods for the nucleotide sequences analysis and specifically for the prediction of nucleotides when knowing the preceding ones in a sequence. In this paper we propose variety of methods and ways when using artificial neural networks at its core to efficiently predict the next sample in the genomic sequence.

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Section: Interf Acementioning

confidence: 99%