Autoregressive Modeling and Feature Analysis of DNA Sequences

Chakravarthy, Niranjan; Spanias, Andreas; Iasemidis, Leonidas D.; Tsakalis, K.

doi:10.1155/s111086570430925x

Cited by 105 publications

(72 citation statements)

References 37 publications

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“…Aynı zamanda ileri ve geri beslemeli lineer tahmin hataları da teorik olarak aynı otoregresif modelleri vermektedirler [10].…”

Section: Reel Haritalama Tekniği (Reel Mapping Technique)unclassified

Sayisal Hari̇talama Tekni̇kleri̇ Ve Fourier Dönüşümü Kullanilarak Dna Di̇zi̇li̇mleri̇ni̇n Siniflandirilmasi

Daş¹,

Türkoğlu²

2016

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Sayısal haritalama tekniklerinin performanslarının karşılaştırılması  DNA dizilimlerinin ekson ve intron olarak sınıflandırılması  Pencereleme fonksiyonlarının sınıflandırma performansına etkisi Makale Bilgileri ÖZET Geliş: 12.06.2015 Kabul: 04.08.2016 DOI: Bir DNA dizilimindeki bazların oluşturdukları kombinasyonlar, o DNA dizilimindeki bir gene karşılık gelir, bu genlerden de RNA kopya dizilimleri çıkarılır. Kopyalanan bu RNA'lar oluşurken genin baz dizilimi baştan sona tümüyle okunmaz. Genlerin okunmayan ve kodlanmayan bölümüne intron, kodlanan kısımlarına ise ekson denir. Bir DNA dizilimindeki protein nerede, ne kadar kodlanır? Büyüme ve gelişme nerede düzenlenir? Kök hücreler nerede başka hücreye dönüştürülür? Tüm bu soruların cevabı ve kanser gibi genetik hastalıkların araştırılması DNA dizilimlerinin ekson ve intron olarak sınıflandırmasıyla mümkündür. Çalışmanın amacı, DNA diziliminin ekson ve intron olarak sınıflandırılmasında farklı sayısal haritalama tekniklerinin performanslarını karşılaştırmaktır. Bu amaç doğrultusunda insan türünün MEFV genine ait DNA dizilimleri, 9 farklı haritalama tekniği ile sayısal dizilere dönüştürülmüştür. Dönüştürülen sayısal dizilerin sınıflandırılmasında Ayrık Fourier Dönüşümü yöntemi kullanılmıştır. Bu yöntemde 4 farklı pencereleme fonksiyonu kullanılmış, sınıflandırma başarımları karşılaştırılmıştır. Ayrıca Fourier tabanlı yöntemle elde edilen sonuçlar, Destek Vektör Makineleri ve K-en yakın komşu algoritması gibi makine öğrenme tabanlı yöntemlerle karşılaştırılmıştır. İnteger haritalama tekniği Ayrık Fourier Dönüşümü yönteminde %96,2 ile diğer makine öğrenme yöntemlerine göre en yüksek sınıflandırma başarımı sağlamıştır. Hamming pencereleme fonksiyonunda sınıflandırma başarısı diğer pencereleme fonksiyonlarından daha yüksek çıkmıştır.

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“…Aynı zamanda ileri ve geri beslemeli lineer tahmin hataları da teorik olarak aynı otoregresif modelleri vermektedirler [10].…”

Section: Reel Haritalama Tekniği (Reel Mapping Technique)unclassified

Sayisal Hari̇talama Tekni̇kleri̇ Ve Fourier Dönüşümü Kullanilarak Dna Di̇zi̇li̇mleri̇ni̇n Siniflandirilmasi

Daş¹,

Türkoğlu²

2016

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…The second choice is based on the geometric representations where meaningful real or complex numbers are assigned to the four characters A, T, G, and C. In this way a single numerical sequence representing the entire character string is obtained. In general FM techniques [5] include the Voss [7], the tetrahedron [2], the complex [8]- [11], the integer [9], the real [12], and the quaternion [13] - [14] representations. Each of the DNA numerical representations offers different properties as will be show in details in the following sections.…”

Section: Fixed Mapping Of Dna Sequencementioning

confidence: 99%

Genomic Analysis and Classification of Exon and Intron Sequences Using DNA Numerical Mapping Techniques

Abo‐Zahhad¹,

Ahmed²,

Abd-Elrahman³

2012

IJITCS

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Abstract-Using digital signal processing in genomic field is a key of solving most problems in this area such as prediction of gene locations in a genomic sequence and identifying the defect regions in DNA sequence. It is found that, using DSP is possible only if the symbol sequences are mapped into numbers. In literature many techniques have been developed for numerical representation of DNA sequences. They can be classified into two types, Fixed Mapping (FM) and Physico Chemical Property Based Mapping (PCPBM ( . The open question is that, which one of these numerical representation techniques is to be used? The answer to this question needs understanding these numerical representations considering the fact that each mapping depends on a particular application. This paper explains this answer and introduces comparison between these techniques in terms of their precision in exon and intron classification. Simulations are carried out using short sequences of the human genome (GRch37/hg19). The final results indicate that the classification performance is a function of the numerical representation method.

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“…In the Paired Numeric representation [6,8], nucleotides (C-G, A-T) are to be paired in a complementary manner and values of -1 and +1 are to be used to denote, respectively, C-G and A-T nucleotide pairs. In the Real Number representation [6,8,13], the nucleotide mappings are C = 0.5, G = -0.5, A = -1.5, and T = 1.5, which bears complementary property. The third group consists of one complex-value numerical representation (as Code 9 in Table 2) called the Complex Number representation [2,5,6,8,[14][15][16], it reflects the complementary nature of C-G and A-T pairs by mapping nucleotides as C = -1-j, G = -1+j, A = 1+j, and T = 1-j.…”

Section: Numerical Representationmentioning

confidence: 99%

“…In this article, nine 1-sequence numerical representations (Codes 1-9) [5][6][7][8][9][10][11][12][13][14][15][16] are identified though a literature search, which can be grouped under positiveinteger-value, real-value, and complex-value numerical representations. In addition, seven 1-sequence complexvalue numerical representations (Codes 10-16) are derived.…”

Section: Introductionmentioning

confidence: 99%

Novel methodologies for spectral classification of exon and intron sequences

Kwan

2012

EURASIP J. Adv. Signal Process.

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Digital processing of a nucleotide sequence requires it to be mapped to a numerical sequence in which the choice of nucleotide to numeric mapping affects how well its biological properties can be preserved and reflected from nucleotide domain to numerical domain. Digital spectral analysis of nucleotide sequences unfolds a period-3 power spectral value which is more prominent in an exon sequence as compared to that of an intron sequence. The success of a period-3 based exon and intron classification depends on the choice of a threshold value. The main purposes of this article are to introduce novel codes for 1-sequence numerical representations for spectral analysis and compare them to existing codes to determine appropriate representation, and to introduce novel thresholding methods for more accurate period-3 based exon and intron classification of an unknown sequence. The main findings of this study are summarized as follows: Among sixteen 1-sequence numerical representations, the K-Quaternary Code I offers an attractive performance. A windowed 1-sequence numerical representation (with window length of 9, 15, and 24 bases) offers a possible speed gain over non-windowed 4-sequence Voss representation which increases as sequence length increases. A winner threshold value (chosen from the best among two defined threshold values and one other threshold value) offers a top precision for classifying an unknown sequence of specified fixed lengths. An interpolated winner threshold value applicable to an unknown and arbitrary length sequence can be estimated from the winner threshold values of fixed length sequences with a comparable performance. In general, precision increases as sequence length increases. The study contributes an effective spectral analysis of nucleotide sequences to better reveal embedded properties, and has potential applications in improved genome annotation.

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Autoregressive Modeling and Feature Analysis of DNA Sequences

Cited by 105 publications

References 37 publications

Sayisal Hari̇talama Tekni̇kleri̇ Ve Fourier Dönüşümü Kullanilarak Dna Di̇zi̇li̇mleri̇ni̇n Siniflandirilmasi

Sayisal Hari̇talama Tekni̇kleri̇ Ve Fourier Dönüşümü Kullanilarak Dna Di̇zi̇li̇mleri̇ni̇n Siniflandirilmasi

Genomic Analysis and Classification of Exon and Intron Sequences Using DNA Numerical Mapping Techniques

Novel methodologies for spectral classification of exon and intron sequences

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