Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases

Wang, Xiaohong; Huan, Jun; Smalter, Aaron; Lushington, Gerald H.

doi:10.1109/bibm.2009.72

Cited by 2 publications

(3 citation statements)

References 22 publications

(7 reference statements)

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“…Through the category of L 2 -norm distance, we achieved the automation possibility of species identification with small sample size sequence [4]. With unavailable RNA sequence of training samples in the number of samples, this study could conduct related calculation of species identification and also supplements how to deal with RNA sequence classification calculations with small samples.…”

Section: Introductionmentioning

confidence: 99%

Species Identification on a Small Sample Size of RNA Sequences by Combined Method of Noise Filtering with L2-Norm

Hu²,

et al. 2011

AEF

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Abstract. This paper proposed a noise filter with L 2 -norm distance method to design a classification of RNA sequences for the species identification, included of the small sample size of the nucleic acid sequence. This method amended and expanded the study by Hu et al. in 2011 [1]. We verified this method with the biological sample "slipper orchids" and its hybrid for biological species identification test. The result is showed that after applied our method, we can distinguish the paternity of a hybrid among a set of samples of "slipper orchids".

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Section: Introductionmentioning

confidence: 99%

Species Identification on a Small Sample Size of RNA Sequences by Combined Method of Noise Filtering with L2-Norm

Hu²,

et al. 2011

AEF

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“…Accordingly, it's not easy for us to get a complete noise-free RNA sequence. But, using the appropriate noise filtering pre-processing of this study enables us to resolve the garbled characters in previously mentioned problems and to enhance the accuracy of automated analysis machines.Through the category of L 2 -norm distance, we achieve the automated species identification with small sample size sequence [4]. With unavailable RNA sequence of trained samples, this study can conduct related calculation of species identification and also supplements how to deal with RNA sequence classification calculations with small samples.…”

mentioning

confidence: 99%

“…Through the category of L 2 -norm distance, we achieve the automated species identification with small sample size sequence [4]. With unavailable RNA sequence of trained samples, this study can conduct related calculation of species identification and also supplements how to deal with RNA sequence classification calculations with small samples.…”

Section: Introductionmentioning

confidence: 99%

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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Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases

Cited by 2 publications

References 22 publications

Species Identification on a Small Sample Size of RNA Sequences by Combined Method of Noise Filtering with <i>L</i><sub>2</sub>-Norm

Species Identification on a Small Sample Size of RNA Sequences by Combined Method of Noise Filtering with <i>L</i><sub>2</sub>-Norm

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

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