[1]YANG Shuai,GUO Maozu,ZHAO Lingling,et al.The method of 100-kernel weight related genes mining in maize mixed with genetic algorithm and XGboost[J].CAAI Transactions on Intelligent Systems,2022,17(1):170-180.[doi:10.11992/tis.202105005]
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The method of 100-kernel weight related genes mining in maize mixed with genetic algorithm and XGboost

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 170-180 Column: 人工智能院长论坛 Public date: 2022-01-05
Title:
The method of 100-kernel weight related genes mining in maize mixed with genetic algorithm and XGboost
Author(s):
YANG Shuai1; 2;  GUO Maozu1; 2;  ZHAO Lingling3;  LI Yang1; 2
1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2. Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing 100044, China;
3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Keywords:
genetic algorithm; eXtreme gradient boosting; machine learning; maize; transcriptome analysis; 100-kernel weight; gene ontology; kyoto encyclopedia of genes and genomes
CLC:
TP391
DOI:
10.11992/tis.202105005
Abstract:
The RNA-Seq-based transcriptome sequencing data has a high feature dimension that requires a lot of computing resources when using traditional methods to find phenotype related genes. Moreover, the range of candidate genes obtained by difference analysis is large, and further screening depends on existing a prior knowledge. A transcriptome analysis method combining genetic algorithm and XGBoost, GA-XGBoost, was proposed to narrow the range of candidate genes for subsequent analysis by incorporating machine learning algorithm. A comparative experiment and subsequent analysis of the gene-100-kernel weight trait association on a set of high-quality maize datasets showed that, compared with training the XGBoost model directly with whole genes and differentially expressed genes, the candidate gene training XGBoost model obtained by the proposed method had the minimum MSE in predicting the 100-kernel weight of maize. Compared with 1542 differentially expressed genes in the results of differential expression analysis, the range of candidate genes was reduced to 48 by the GA-XGBoost method, which was reduced by 31 times, indicating that the proposed method could effectively improve the ability and efficiency of transcriptome data analysis.
References:
[1] MOROZOVA O, HIRST M, MARRA M A. Applications of new sequencing technologies for transcriptome analysis[J]. Annual review of genomics and human genetics, 2009, 10: 135–151.
[2] 郭茂祖, 杨帅, 赵玲玲. 基于RNA-Seq的转录组分析方法[J]. 计算机科学, 2020, 47(11A): 35–39.GUO Maozu, YANG Shuai, ZHAO Lingling. Transcriptome analysis method based on RNA-Seq[J]. Computer science, 2020, 47(11A): 35–39.
[3] KUKURBA K R, MONTGOMERY S B. RNA sequencing and analysis[J]. Cold spring harbor protocols, 2015, 2015(11): 951–961.
[4] XU Maoqi, CHEN Liang. An empirical likelihood ratio test robust to individual heterogeneity for differential expression analysis of RNA-seq[J]. Briefings in bioinformatics, 2018, 19(1): 109–117.
[5] ZHAO Xin, DOU Jian, CAO Jinglin, et al. Uncovering the potential differentially expressed miRNAs as diagnostic biomarkers for hepatocellular carcinoma based on machine learning in The Cancer Genome Atlas database[J]. Oncology reports, 2020, 43(6): 1771–1784.
[6] 白云帆. 基于网络分析和机器学习的肝癌中糖链相关基因筛选[D]. 哈尔滨: 哈尔滨工业大学, 2019.BAI Yunfan. Screening of sugar chain related genes in hepatocellular carcinoma based on network analysis and machine learning[D]. Harbin: Harbin Institute of Technology, 2019.
[7] SANTOS C A, ANDRADE S C S, TEIXEIRA A K, et al. Transcriptome differential expression analysis reveals the activated genes in Litopenaeus vannamei shrimp families of superior growth performance[J]. Aquaculture, 2021, 531: 735871.
[8] ABBAS S Z, QADIR M I, MUHAMMAD S A. Systems-level differential gene expression analysis reveals new genetic variants of oral cancer[J]. Scientific reports, 2020, 10(1): 14667.
[9] HOLLAND J. Adaptation in natural and artificial systems: an introductory analysis with application to biology [J]. Control & Artificial Intelligence, 1975:1–198.
[10] XING Xuguang, LIU Ye, GARG A, et al. An improved genetic algorithm for determining modified water-retention model for biochar-amended soil[J]. CATENA, 2021, 200: 105143.
[11] REVUELTA E C, CHáVEZ M J, VERA J A B, et al. Optimization of laser scanner positioning networks for architectural surveys through the design of genetic algorithms[J]. Measurement, 2021, 174: 108898.
[12] LIU Kunhong, JIN Qiushi, XU Huangchao, et al. Micro-expression recognition using advanced genetic algorithm[J]. Signal processing: image communication, 2021, 93: 116153.
[13] XUE Jingjing, AHMADIAN R, JONES O, et al. Design of tidal range energy generation schemes using a Genetic Algorithm model[J]. Applied energy, 2021, 286: 116506.
[14] YARSYARSKY P. Using a genetic algorithm to fit parameters of a COVID-19 SEIR model for US states[J]. Mathematics and computers in simulation, 2021, 185: 687–695.
[15] PENG Cheng, WU Xinyu, YUAN Wen, et al. MGRFE: multilayer recursive feature elimination based on an embedded genetic algorithm for cancer classification[J]. IEEE/ACM transactions on computational biology and bioinformatics, 2021, 18(2): 621–632.
[16] AKARSU H, AGUILAR-BULTET L, FALQUET L. deltaRpkm: an R package for a rapid detection of differential gene presence between related bacterial genomes[J]. BMC bioinformatics, 2019, 20(1): 621.
[17] KUMAR S, KALRA S, SINGH B, et al. RNA-Seq mediated root transcriptome analysis of Chlorophytum borivilianum for identification of genes involved in saponin biosynthesis[J]. Functional & integrative genomics, 2016, 16(1): 37–55.
[18] SUN Qiuli, ZHAO Chunpeng, WANG Tianyun, et al. Expression profile analysis of long non-coding RNA associated with vincristine resistance in colon cancer cells by next-generation sequencing[J]. Gene, 2015, 572(1): 79–86.
[19] MA Wentai, LIU Zhaoyu, CHEN Xiaozhou, et al. A protein identification algorithm for tandem mass spectrometry by incorporating the abundance of mRNA into a binomial probability scoring model[J]. Journal of proteomics, 2019, 197: 53–59.
[20] CHEN Tianwu, GUESTRIN C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference. San Francisco, USA, 2016: 785–794.
[21] JIANG Yiquan, CAO Su’e, CAO Shilei, et al. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning[J]. Journal of cancer research and clinical oncology, 2021, 147(3): 821–833.
[22] ABDU-ALJABAR R D, AWAD O A. A Comparative analysis study of lung cancer detection and relapse prediction using XGBoost classifier[J]. IOP conference series: materials science and engineering, 2021, 1076: 012048.
[23] CHEN Sijie, ZHOU Wenjing, TU Jinghui, et al. A novel XGBoost method to infer the primary lesion of 20 solid tumor types from gene expression data[J]. Frontiers in genetics, 2021, 12: 632761.
[24] ZHAO Xiaowei, ZHANG Ye, NING Qiao, et al. Identifying N6-methyladenosine sites using extreme gradient boosting system optimized by particle swarm optimizer[J]. Journal of theoretical biology, 2019, 467: 39–47.
[25] GUYON I, WESTON J, BARNHILL S, et al. Gene selection for cancer classification using support vector machines[J]. Machine learning, 2002, 46(1/2/3): 389–422.
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