A particle swarm optimization-aided fuzzy cloud classifier applied for plant numerical taxonomy based on attribute similarity

Lü, Hongfei; Pi, Erxu; Peng, Qiu-Fa; Wang, Lanlan; Zhang, Changjiang

doi:10.1016/j.eswa.2008.12.065

Cited by 22 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The genus Camellia contains many economic species, including the well-known Camellia sinensis (tea) and other ornamental plants, such as Camellia japonica and Camellia sasanqua (Kole, 2011;Lu et al, 2009). Camellia oleifera, as a woody oil plant, has attracted considerable attention because of the abundant edible oils with high monounsaturated fatty acid content in seeds that have beneficial effects on human health (Lin & Fan, 2011) (Figure 1a).…”

Section: Introductionmentioning

confidence: 99%

Chromosome‐level genome of Camellia lanceoleosa provides a valuable resource for understanding genome evolution and self‐incompatibility

et al. 2022

View full text Add to dashboard Cite

The section Oleifera (Theaceae) has attracted attention for the high levels of unsaturated fatty acids found in its seeds. Here, we report the chromosome-scale genome of the sect. Oleifera using diploid wild Camellia lanceoleosa with a final size of 3.00 Gb and an N50 scaffold size of 186.43 Mb. Repetitive sequences accounted for 80.63% and were distributed unevenly across the genome. Camellia lanceoleosa underwent a whole-genome duplication event approximately 65 million years ago (65 Mya), prior to the divergence of C. lanceoleosa and Camellia sinensis (approx. 6-7 Mya). Syntenic comparisons of these two species elucidated the genomic rearrangement, appearing to be driven in part by the activity of transposable elements. The expanded and positively selected genes in C. lanceoleosa were significantly enriched in oil biosynthesis, and the expansion of homomeric acetyl-coenzyme A carboxylase (ACCase) genes and the seed-biased expression of genes encoding heteromeric ACCase, diacylglycerol acyltransferase, glyceraldehyde-3phosphate dehydrogenase and stearoyl-ACP desaturase could be of primary importance for the high oil and oleic acid content found in C. lanceoleosa. Theanine and catechins were present in the leaves of C. lanceoleosa. However, caffeine can not be dectected in the leaves but was abundant in the seeds and roots. The functional and transcriptional divergence of genes encoding SAM-dependent N-methyltransferases may be associated with caffeine accumulation and distribution. Gene expression profiles, structural composition and chromosomal location suggest that the late-acting self-incompatibility of C. lanceoleosa is likely to have favoured a novel mechanism co-occurring with gametophytic self-incompatibility. This study provides valuable resources for quantitative and qualitative improvements and genome assembly of polyploid plants in sect. Oleifera.

show abstract

Section: Introductionmentioning

confidence: 99%

Chromosome‐level genome of Camellia lanceoleosa provides a valuable resource for understanding genome evolution and self‐incompatibility

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Camellia is a large genus of family Theaceae with many species of significant economic and scientific value [1] . Some Camellia species are used to produce green tea, a popular beverage.…”

Section: Introductionmentioning

confidence: 99%

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Lü

Ghiassi

et al. 2012

PLoS ONE

Self Cite

View full text Add to dashboard Cite

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species.

show abstract

“…However, most of the techniques were implemented for the analysis of molecular sequences. Most recently, two new techniques have been described for inferring phylogenetic trees by using answer set programming [5] and by particle swarm optimization-aided fuzzy cloud classifier [6]. The both methods give optimum solutions to find a subset of characters with minimum number of features.…”

Section: Introductionmentioning

confidence: 99%

Optimization of morphological data in numerical taxonomy analysis using genetic algorithms feature selection method

Bakis

Sezerman

Babaç

et al. 2009

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

View full text Add to dashboard Cite

Studies in Numerical Taxonomy are carried out by measuring characters as much as possible. The workload over scientists and labor to perform measurements will increase proportionally with the number of variables (or characters) to be used in the study. However, some part of the data may be irrelevant or sometimes meaningless. Here in this study, we introduce an algorithm to obtain a subset of data with minimum characters that can represent original data. Morphological characters were used in optimization of data by Genetic Algorithms Feature Selection method. The analyses were performed on an 18 character*11 taxa data matrix with standardized continuous characters. The analyses resulted in a minimum set of 2 characters, which means the original tree based on the complete data can also be constructed by those two characters.

show abstract

A particle swarm optimization-aided fuzzy cloud classifier applied for plant numerical taxonomy based on attribute similarity

Cited by 22 publications

References 31 publications

Chromosome‐level genome of Camellia lanceoleosa provides a valuable resource for understanding genome evolution and self‐incompatibility

Chromosome‐level genome of Camellia lanceoleosa provides a valuable resource for understanding genome evolution and self‐incompatibility

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Optimization of morphological data in numerical taxonomy analysis using genetic algorithms feature selection method

Contact Info

Product

Resources

About