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Plant Responses and Tolerance to Drought
Published in Hasanuzzaman Mirza, Nahar Kamrun, Fujita Masayuki, Oku Hirosuke, Tofazzal M. Islam, Approaches for Enhancing Abiotic Stress Tolerance in Plants, 2019
Sumit Jangra, Aakash Mishra, Priti, Kamboj Disha, Neelam R. Yadav, Ram C. Yadav
The progress in next-generation sequencing technologies brought about advancement in linkage mapping population from biparental to the second generation multiparent populations (Bohra, 2013), such as Nested Association Mapping (NAM) population (Yu et al., 2008) and Multiparent Advanced Generation Intercross (MAGIC) population (Kover et al., 2009), to exploit allelic diversity from different parents for fine mapping of QTLs. Use of these multiparent populations with advanced computer modeling systems provides opportunities in utilizing the advantages of both linkage and association mapping for crop improvement (Bevan and Uauy, 2013). A germplasm-based population “PMiGAP” (Pearl millet Inbred Germplasm Association Panel) has been developed in pearl millet to harness much more genetic variations of traits (Sehgal et al., 2015).
Machine Learning in Healthcare
Published in Om Prakash Jena, Bharat Bhushan, Nitin Rakesh, Parma Nand Astya, Yousef Farhaoui, Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems, 2022
Finding association means trying to find relationships between variables, particularly in huge and complex data sets, association mapping is very critical. Association rule method which is used for finding the relationships between variables in the large database [33]. Clustering is a method that clusters similar objects together by finding common elements between the different data and arrange them as group or batch. The presence or absence of common elements in the data object also helps in categorizing of data and creating clusters [34–36]. There are different types of clustering that is exclusive, agglomerative, overlapping and probabilistic.
Organ-specific expression revealed using support vector machine on maize nested association mapping datasets
Published in Yuli Rahmawati, Peter Charles Taylor, Empowering Science and Mathematics for Global Competitiveness, 2019
U.M. Mu’inah, R. Fajriyah, H. Nugrahapraja
We have performed the SVM analysis on gene expression datasets of maize nested association mapping. As a result, the SVM analysis showed the classification results from the testing data indicate that there are two errors in the classification in class 1 (ear) and class 2 (root). However, the accuracy value from the testing data is 95% with an AUC value of 0.989. In summary, the high accuracy value indicates the SVM model is a perfect model to classify the organ-specificity of the maize dataset. Further study can be done to compare the results to other algorithms.
Mobile 3D assembly process information construction and transfer to the assembly station of complex products
Published in International Journal of Computer Integrated Manufacturing, 2018
Hong Xiao, Yugang Duan, Zhongbo Zhang
Based on this definition, the key of ARAO construction is establishing the association mapping and . Normally, based on the semantic information, such as the type of assembly tool and operation, function, etc., most of the assembly process can be decomposed into a series of assembly steps, which can form ARAO templates and be reused. Figure 9 demonstrates the ARAO templates corresponding to the assembly steps in Figure 8. Apparently, ARAO templates can demonstrate the API much more intuitively. Similarly, can be established based on semantics. Figure 10 shows the association mapping between the ARAO template and the corresponding part or component it acts on.
Cadmium contamination in food crops: Risk assessment and control in smart age
Published in Critical Reviews in Environmental Science and Technology, 2023
Yan Huili, Zhang Hezifan, Hao Shuangnan, Wang Luyao, Xu Wenxiu, Ma Mi, Luo Yongming, He Zhenyan
Joint linkage association mapping (JLAM) and genome-wide association studies (GWAS) are the most common methods applied to excavate low-Cd related natural variations and quantitative trait loci (QTLs). JLAM is a method combining the advantages of linkage mapping and association mapping. In practice, the effects of QTLs measured by their linkage high-density molecular markers to target traits were calculated analyzed in a population. By using artificial mapping population, JLAM can effectively and accurately excavate QTLs and natural variations related to low-Cd accumulation (Würschum et al., 2012). To date, efforts in JLAM have yielded many successes, over 30 Cd accumulation related QTLs have been excavated in maize, rice and wheat (Table 1), including many important Cd transporters. For example, OsHMA3, a rice gene isolated using the Anjana Dhan and Nipponbare derived population, is located in the interval defined by RM21251 and RM21275 (Ueno et al., 2010). Using a double haploid (DH) rice population derived from TN1 and CJ0620, the defensin-like protein, CAL1, was identified (Luo et al., 2018). In maize and barley, ZmHMA3 was identified by fine mapping with bulked sergeant RNA-seq analysis using a biparental segregating maize population of Jing724 (low-Cd line) and Mo17 (high-Cd line) (Tang et al., 2021) and HvHMA3 was fine-mapped from a cross between BCS318 and Haruna Nijo (Lei et al., 2020). At present, populations used in natural low-Cd variation excavation are mainly double-parent populations such as DH (Luo et al., 2018), recombinant inbred lines (RIL) (Oladzad-Abbasabadi et al., 2018), chromosome segment substitution lines (CSSL) (Abe et al., 2013), in which limited genetic variants have been obtained. A more comprehensive excavation of natural low-Cd variation needs more complex artificial multiparent population like complete-diallel plus unbalanced breeding-derived inter-cross (CUBIC) (Liu, Wang, et al., 2020) population and nested associated mapping (NAM) population (Gage et al., 2020).
Diabetic retinopathy progression associated with haplotypes of two VEGFA SNPs rs2010963 and rs699947
Published in Egyptian Journal of Basic and Applied Sciences, 2023
Haider Ali Alnaji, Rabab Omran, Aizhar H. Hasan, Mohammed Qasim Al Nuwaini
are from each other have strong LD. The haplotype and LD are important for large-scale association-mapping studies [39]. unpredictable; two loci near each other might have weak LD, whereas loci of different SNPs that are so far from each other have strong LD. The haplotype and LD are important for large-scale association mapping studies [40].