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Prediction of 1000-grain Weight of Rapeseed Based on Auto-encoder

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成果类型:
期刊论文、会议论文
作者:
Xuyan He;Changhua Liu;Wenjie Guan
通讯作者:
Liu, Changhua(liuch@whpu.edu.cn)
作者机构:
[He X.; Liu C.] School of Mathematics and Computer Science, Wuhan Polytechnic University, Hubei, Wuhan, China
[Guan W.] Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Hubei, Wuhan, China
语种:
英文
关键词:
Bioinformatics;Data reduction;Deep learning;Forecasting;Genes;Learning systems;Oilseeds;Principal component analysis;Signal encoding;1000-grain weight of rapesed;Auto encoders;Deep learning;Dimensionality reduction;Gene prediction;Genetic data;Grain weights;High-dimensional;Higher-dimensional;Single nucleotide polymorphisms;Errors
期刊:
ACM International Conference Proceeding Series
年:
2022
页码:
1–6
会议论文集名称:
CSAE '22: Proceedings of the 6th International Conference on Computer Science and Application Engineering
出版地:
New York, NY, United States
出版者:
Association for Computing Machinery
ISBN:
9781450396004
机构署名:
本校为第一机构
院系归属:
数学与计算机学院
摘要:
DNA sequence contains a lot of genetic information, and a variety of phenotypic information of the organism can be obtained by analyzing its single nucleotide polymorphism (SNP). Experiments have shown that 1000-grain weight of rapeseed is positively correlated with its oil yield. In this paper, the 1000-grain weight of rapeseed at maturity is predicted by the genetic data of rapeseed, so as to control the oil yield of rapeseed. When analyzing and processing high-dimensional genetic data, the author proposes a deep learning method—auto-encoder...

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