[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122632-en":3,"doc-seo-122632-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122632,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Comparative analysis of tissue-specific genes in maize based on machine learning models - CNN performs technically best, LightGBM performs biologically soundest","Comparative analysis evaluates how linear models (Limma), machine learning (LightGBM), and deep learning (CNN) identify tissue-specific genes from large-scale maize multi-tissue RNA-seq datasets. An expression matrix is processed using information gain and SHAP-based strategies, then validated by k-means clustering complementarity (V-measure), GO analysis, and literature retrieval. Results show CNN achieves higher technical performance, LightGBM highlights key transcription factors, and combined gene sets yield 78 biologically significant core tissue-specific genes.","TYPE Original Research PUBLISHED 09 May 2023  \nDOI 10.3389/fgene.2023.1190887  \nOPEN ACCESS  \nEDITED BY  \nHao Cheng,  \nUniversity of California, Davis, United States  \nREVIEWED BY  \nXiujun Zhang,  \nWuhan Botanical Garden (CAS), China Indika Kahanda,  \nUniversity of North Florida, United States  \n*CORRESPONDENCE  \nZijie Wang,  \nComparative analysis of  \ntissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest  \n [wangzj55@mail2.sysu.edu.cn](wangzj55@mail2.sysu.edu.cn)  \n†These authors have contributed equally to this work and share last authorship  \nRECEIVED 21 March 2023  \nACCEPTED 17 April 2023  \nPUBLISHED 09 May 2023  \nCITATION  \nWang Z, Zhu Y, Liu Z, Li H, Tang X and Jiang Y (2023), Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest.  \nFront. Genet. 14:1190887 .  \ndoi: 10.3389/fgene.2023.1190887  \nCOPYRIGHT  \n© 2023 Wang, Zhu, Liu, Li, Tang and Jiang. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nZijie Wang 1*, Yuzhi Zhu 1, Zhule Liu 1†, Hongfu Li 1†, Xinqiang Tang 2† and Yi Jiang 1†  \n1School of Agriculture, Sun Yat-sen University, Shenzhen, China, 2School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China  \nIntroduction: With the advancement of RNA-seq technology and machine learning, training large-scale RNA-seq data from databases with machine learning models can generally identify genes with important regulatory roles that were previously missed by standard linear analytic methodologies. Finding tissue-specific genes could improve our comprehension of the relationship between tissues and genes. However, few machine learning models fortranscriptome data have been deployed and compared to identify tissuespecific genes, particularly for plants.  \nMethods: In this study, an expression matrix was processed with linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN) with information gain and the SHAP strategy based on 1,548 maize multi-tissue RNA-seq data obtained from a public database to identify tissue-specific genes. In terms of validation, V-measure values were computed based on k-means clustering of the gene sets to evaluate their technical complementarity. Furthermore, GO analysis and literature retrieval were used to validate the functions and research status of these genes.  \nResults: Based on clustering validation, the convolutional neural network outperformed others with higher V-measure values as 0.647, indicating that its gene set could cover as many specific properties of various tissues as possible, whereas LightGBM discovered key transcription factors. The combination of three gene sets produced 78 core tissue-specific genes that had previously been shown in the literature to be biologically significant.  \nDiscussion: Different tissue-specific gene sets were identified due to the distinct interpretation strategy for machine learning models and researchers may use multiple methodologies and strategies for tissue-specific gene sets based on their goals, types of data, and computational resources. This study provided comparative insight for large-scale data mining of transcriptome datasets, shedding light on resolving high dimensions and bias difficulties in bioinformatics data processing.  \nFrontiers in Genetics 01 [frontiersin.org](frontiersin.org)  \nWang et al. 10.3389/fgene.2023.1190887  \nKEYWORDS  \ntissue specific genes, maize, CNN, limma, SHAP (shapley additive explanation), ","cbCaii81Bjweif65","https://ap.wps.com/l/cbCaii81Bjweif65","pdf",3005736,1,15,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion","[{\"question\":\"Which models were compared for identifying tissue-specific genes in maize?\",\"answer\":\"The study compares linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN).\"},{\"question\":\"How were the identified gene sets validated?\",\"answer\":\"Validation used k-means clustering complementarity with V-measure, GO analysis, and literature retrieval to confirm gene functions and research relevance.\"},{\"question\":\"What were the main findings about model performance and biological significance?\",\"answer\":\"CNN showed the best technical performance via higher V-measure, LightGBM uncovered key transcription factors, and combining gene sets produced 78 core tissue-specific genes previously reported as biologically significant.\"}]","Comparative analysis of tissue-specific genes in maize based on machine learning models - 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