Functional site prediction on the DNA sequence by artificial neural networks

Hatzigeorgiou, Artemis G.; Mache, Niels; Reczko, Martin

doi:10.1109/ijsis.1996.565045

Cited by 24 publications

(23 citation statements)

References 14 publications

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“…The way that NetGene2 extracts information makes it a complex way of predicting the structure of a gene. Bipin Nair [15] propose an efficient tool to analyze the possibility of getting affected by NonSmall Cell Lung Cancer (NSCLC) by comparing Lung Cancer microRNAs (LC-miRNAs) structures. Here we use global optimal alignment and Target Scan for target comparison and binding location detection.…”

Section: Litrature Surveymentioning

confidence: 99%

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Coding and functional defect region prediction of placental protein in an embryo cell of first trimester using ANN approach

Nair¹,

Reghunath²

2018

IJET

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The protein coding and functional regions in DNA sequences has become an exciting task in bioinformatics. In particular, the coding region has a 3-base periodicity, which helps for exon identification. Many signal processing tools and techniques have been successfully applied to identify tasks, but still need to be improved in this direction. In our work, we employ ANN classifier to predict coding and functional region of protein in human embryo cell protein in first trimester, and evaluate their performances according to the comparison energy levels of coding region. The obtained from the threshold energy level, results show that in a box plot finally predict the mutation.

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Section: Litrature Surveymentioning

confidence: 99%

“…And they aligned the sequence of these miRNAs to observe and analyze matches, mismatch, and gaps with the normal miRNA sequences. Hatzigeorgiou [17] they focus on using Back-percolation, Cascade-Related and Time Delay neural networks. This method provides a much efficient generalization than the known backpropagation algorithm.…”

Section: Litrature Surveymentioning

confidence: 99%

Coding and functional defect region prediction of placental protein in an embryo cell of first trimester using ANN approach

Nair¹,

Reghunath²

2018

IJET

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“…Unfortunately, the detection of SS is often complicated because of common occurrences of consensus di-nucleotides at sites other than the SS. Various computational techniques and algorithms have recently been developed for SS detection: neural network approaches [4], [11], [14], [29], [34], probabilistic models [6], [26], [38], and techniques based on discriminant analysis [39]. These methods primarily seek consensus motifs or features surrounding the SS by deriving a priori models from training samples [16].…”

Section: Splice Sites (Ss)mentioning

confidence: 99%

Markov Encoding for Detecting Signals in Genomic Sequences

Rajapakse

2005

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-We present a technique to encode the inputs to neural networks for the detection of signals in genomic sequences. The encoding is based on lower-order Markov models which incorporate known biological characteristics in genomic sequences. The neural networks then learn intrinsic higher-order dependencies of nucleotides at the signal sites. We demonstrate the efficacy of the Markov encoding method in the detection of three genomic signals, namely, splice sites, transcription start sites, and translation initiation sites.

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“…There are many methods for splice site prediction, such as hidden Markov model [1], combinatorial methods [2], support vector machine [3], genetic algorithm [4], grammar based algorithms [5] artificial neural network [6][7][8][9][10][11] and neural network hybrid methods [12][13][14][15][16][17]. Neural networks have been widely used in splice site detection methods because of their ability to learn and solve many real time problems.…”

Section: Introductionmentioning

confidence: 99%

“…In a different method pair-wise correlation of di-nucleotides at splice site consensus is used as input to the neural network [7]. The main problem with these methods is their high false positive rate.…”

Section: Introductionmentioning

confidence: 99%

Splice Site Detection in DNA Sequences using Probabilistic Neural Network

Nassa¹,

Singh²,

Goel³

2013

IJCA

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With numerous of genomes sequenced, gene prediction has become a challenging problem in bioinformatics. Gene prediction helps in identifying physical and mental features of different organisms. A large number of gene prediction tools have been developed in the past two decades. Splice site detection method lies at the heart of ab-initio gene prediction tools and plays an important role in detecting the exon boundaries. In this paper, a method for detecting splice sites by using generalized regression neural network is proposed. The proposed method uses conditional probabilities to preprocess the input which enables it to incorporate the already known sequence features from biological knowledge. The experimental results show that the application of this new architecture to splice site detection has greatly improved the training time and reduces the false positive predictions.

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Functional site prediction on the DNA sequence by artificial neural networks

Cited by 24 publications

References 14 publications

Coding and functional defect region prediction of placental protein in an embryo cell of first trimester using ANN approach

Coding and functional defect region prediction of placental protein in an embryo cell of first trimester using ANN approach

Markov Encoding for Detecting Signals in Genomic Sequences

Splice Site Detection in DNA Sequences using Probabilistic Neural Network

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