Digital signal processing for gene prediction

Tomar, Vikrant Singh; Gandhi, Deep; Vijaykumar, C.

doi:10.1109/tencon.2008.4766648

Cited by 6 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The existence of nucleotide is specified as '1' and nonexistence as '0' using conversion process. Accuracy in forecast of genic segments is increased by decreasing noise which is a key in pre-processing [5].…”

Section: Proposed Genome Bio-informatics Systemmentioning

confidence: 99%

“…Learning about the principal structure of coding regions for proteins helps their secondary and the tertiary structure. Once the arrangement of exon segments is completely studied, probability of perceiving whole anomalies helps in preparation of drugs and treat ailments [4], [5]. Hence entire alive beings are classified into prokaryotes as well as eukaryotes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New Exon Prediction Techniques Using Adaptive Signal Processing Algorithms for Genomic Analysis

2019

View full text Add to dashboard Cite

Substantial research and monetary aids to healthcare establishments are provided by cloud computing. A benign position to store and handle vast genome data is offered by cloud services. Labs for gene sequencing send out raw and contingent data over the Internet to multiple sequence collections under conservative flow of gene information. The use of cloud services also reduces the storage costs of deoxyribonucleic acid (DNA) sequencing. Here, an efficient and new bio-informatics genomic system is proposed by the use of cloud services from Amazon to access the stored gene data and process it. A key task in bio-informatics is to locate protein-coding sections in a gene sequence based on three base periodicity (TBP) is for disease diagnosis and design drugs. Here, a novel cloud-based adaptive exon predictor (AEP) using Amazon cloud services is proposed to improve the accuracy in exon finding ability as well as aimed at superior convergence. Noise in the input gene sequence given to the proposed AEPs is pre-processed using normalized LMS filtering. Computational complexity can be reduced using proposed data normalized form of least logarithmic absolute difference (NLLAD) algorithm and its error normalized variants. It was shown that sign regressor NLLAD (SRNLLAD) dependent AEP is efficient in exon forecast applications using different metrics for a performance like sensitivity 0.8037, precision 0.8052 along with specificity 0.8146 by different gene sequences considered from the National Center for Biotechnology Information (NCBI) databank. The proposed AEPs have shown upright performance than typical LMS and other AEPs in terms of exon prediction accuracy, convergence, and computational complexity. Their less computational complexity will be found attractive, and they are suitable to use in bio-informatics nano devices.INDEX TERMS Amazon cloud services, bio-informatics, convergence, deoxyribonucleic acid, National Center for Biotechnology Information, base three periodicity.

show abstract

Section: Proposed Genome Bio-informatics Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Exon Prediction Techniques Using Adaptive Signal Processing Algorithms for Genomic Analysis

2019

View full text Add to dashboard Cite

show abstract

“…[10] introduced positional frequency distribution of nucleotides (PFDN) algorithm for prediction of coding region. Tomar et al [11] used harmonic suppression and adaptive MV filters for minimization of power in intron regions and was able to detect smaller exons. The authors in this article present a comparative performance analysis of anti-notch filter with lattice, cascaded lattice and harmonic suppression comb structure for identification of coding regions of a DNA sequence.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis and simulation of IIR anti-notch filter with various structures for gene prediction application

Barman

Biswas

Das

et al. 2012

2012 5th International Conference on Computers and Devices for Communication (CODEC)

View full text Add to dashboard Cite

Gene prediction is an important topic in genomic research. Various techniques are in use for identification of protein coding regions in genes. Application of Fourier technique is one of the most popular methods of gene prediction, in which prediction algorithm is based on period-3 property of DNA where it exhibits a prominent peak in coding region. Spectrum estimation by Fourier method generates various harmonics, generally known as 1/f noise along with sharp peaks, which may lead to false prediction of coding regions. Researchers used various parametric and non-parametric filters to tackle this problem and improve the accuracy of prediction. Performance of anti-notch filter with cascaded lattice structure on one hand and harmonic suppressor with comb filter on the other hand have been compared here for identification of coding regions of Celegan F56F11.4a chromosome. The authors have analyzed the performance in terms of standard deviation and signal-to-noise ratio. A Matlab simulink environment has been used for filter realization and performance analysis.

show abstract

A new algorithm for predicting protein coding regions based on the hybird threshold

Che

et al. 2012

2012 5th International Conference on BioMedical Engineering and Informatics

View full text Add to dashboard Cite

Digital signal processing for gene prediction

Cited by 6 publications

References 19 publications

New Exon Prediction Techniques Using Adaptive Signal Processing Algorithms for Genomic Analysis

New Exon Prediction Techniques Using Adaptive Signal Processing Algorithms for Genomic Analysis

Performance analysis and simulation of IIR anti-notch filter with various structures for gene prediction application

A new algorithm for predicting protein coding regions based on the hybird threshold

Contact Info

Product

Resources

About