The Modified DNA Identification Classification on Fuzzy Relation

Hu, Yang; Hu, Yuh Hua; Ke, Jyh Bin

doi:10.4028/www.scientific.net/amm.48-49.1275

Cited by 5 publications

(11 citation statements)

References 31 publications

(29 reference statements)

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“…This study found that we can easily and efficiently differentiate the species relationships of "slipper orchids" samples by this method, which modified Hu et al's study [1]. They explored the sequence analysis but didn't mention the method which might fail.…”

Section: Introductionmentioning

confidence: 94%

“…This method amends and expands the study of Hu et al in 2011 [1]. We verify this method with the biological sample "slipper orchids" and its hybrid for biological species identification test.…”

mentioning

confidence: 91%

“…From [8], we know what constitute the 20 amino acids of biological proteins. Hence, we adopted the method of Hu et al [1] and set the codes of 20 kinds of amino acids as parameters 1 2 0 x x . Parameters are UAA, UAG, UGA and other "STOP" string as a characteristic frequency of occurrence.…”

Section: ) Base Sequence Noise Filtering Methodsmentioning

confidence: 99%

“…This study amends the classification of RNA sequences proposed by Hu et al in 2011 [1], launching mathematical analysis to solve the garbled problem due to the small sample electrophoretic analysis of nucleic acid sequences. RNA electrophoresis analysis has the characteristics of negatively charged nucleic acids which cross the gel in the electric field and move towards the cathode.…”

Section: Introductionmentioning

confidence: 99%

“…They explored the sequence analysis but didn't mention the method which might fail. That is, A small sample size of RNA sequence may not be successfully classified by artificial intelligence methods with the mathematical calculation [1] [12]. Pre-processing and noise filtering can solve garbled electrophoresis and effectively resolve the problem of automated RNA sequencing analysis [14] [16] [17].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The Identification on A Small Sample Size of RNA Sequences by Combined Method of Noise Filtering with L2-norm

Hu¹,

Hu²,

Ke³

et al. 2011

IJAPM

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Abstract-This paper proposes a noise filter with L 2 -norm distance method to design a classification of RNA sequences for the species identification, including of the small sample size of the nucleic acid sequence. This method amends and expands the study of Hu et al. in 2011 [1]. We verify this method with the biological sample "slipper orchids" and its hybrid for biological species identification test. The result shows that we can distinguish the paternity of a hybrid among a set of samples of "slipper orchids" by using this method.Index Terms-L 2 -norm distance; nucleic acid sequence; species identification I. INTRODUCTIONThis method is mainly based on L 2 -norm distance to classify the amino acid sequences, to do pre-processing filtering noise toward the non-A, U, C, G character analyzed through electrophoresis, and to check the progeny of hybrid. This study found that we can easily and efficiently differentiate the species relationships of "slipper orchids" samples by this method, which modified Hu et al.'s study [1]. They explored the sequence analysis but didn't mention the method which might fail. That is, A small sample size of RNA sequence may not be successfully classified by artificial intelligence methods with the mathematical calculation [1] [12]. Pre-processing and noise filtering can solve garbled electrophoresis and effectively resolve the problem of automated RNA sequencing analysis [14] [16] [17]. Consequently, further expansion of the species can truly be applied to biological classification, as Table 2-3.In the past, the "morphological" observation method [3] was widely adopted to make species identification toward animals and plants. However, the conditions necessary for such identification are very strict. There must be a complete animal and plant appearance or the characteristics parts of that type of animal and plant [2]. RNA records genetic characteristics of organisms, and various species have different genetic composition. Also, different individuals of the same species can be distinguished through RNA analysis.This study amends the classification of RNA sequences proposed by Hu et al. in 2011 [1], launching mathematical analysis to solve the garbled problem due to the small sample electrophoretic analysis of nucleic acid sequences. RNA electrophoresis analysis has the characteristics of negatively charged nucleic acids which cross the gel in the electric field and move towards the cathode. Because of different Manuscript received May 16, 2011; revised June 20, 2011. molecular weights, the gel pore size varies in the speed of movement, so as to separate the different sizes of nucleic acids. However, RNA sequencing generally employs vertical electrophoresis [13]. The range of gel electrophoresis analysis can analyze from several nucleotides to millions of chromosomal RNA of nucleotides. However, it has a resolving power within a certain range and can't analyze any RNA fragments of various sizes with a colloid. Therefore, to obtain excellent resolving power, we must explore the range ...

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Section: Introductionmentioning

confidence: 94%

mentioning

confidence: 91%

Section: ) Base Sequence Noise Filtering Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The Identification on A Small Sample Size of RNA Sequences by Combined Method of Noise Filtering with L2-norm

Hu¹,

Hu²,

Ke³

et al. 2011

IJAPM

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A Comprehensive Review on Computer Vision and Fuzzy Logic in Forensic Science Application

et al. 2022

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A Study on Hybridization of Intelligent Techniques in Bioinformatics

Bhargavi

Jyothi

Mamatha

2017

Advances in Data Mining and Database Management

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This chapter aims to study the use of Hybridization of intelligent techniques in the areas of bioinformatics and computational molecular biology. These areas have risen from the needs of biologists to utilize and help interpret the vast amounts of data that are constantly being gathered in genomic research. Also describes the kind of methods which were developed by the research community in order to search, classify and mine different available biological databases and simulate biological experiments. This chapter also presents the hybridization of intelligent systems involving neural networks, fuzzy systems, neuro-fuzzy system, rough set theory, swam intelligence and genetic algorithm. The key idea was to demonstrate the evolution of intelligence in bioinformatics. The developed hybridization of intelligent techniques was applied to the real world applications. The hybridization of intelligent systems performs better than the individual approaches. Hence these approaches might be extremely useful for hardware implementations.

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The Modified DNA Identification Classification on Fuzzy Relation

Cited by 5 publications

References 31 publications

The Identification on A Small Sample Size of RNA Sequences by Combined Method of Noise Filtering with L2-norm

The Identification on A Small Sample Size of RNA Sequences by Combined Method of Noise Filtering with L2-norm

A Comprehensive Review on Computer Vision and Fuzzy Logic in Forensic Science Application

A Study on Hybridization of Intelligent Techniques in Bioinformatics

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