{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T14:53:22Z","timestamp":1783522402469,"version":"3.55.0"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T00:00:00Z","timestamp":1652745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T00:00:00Z","timestamp":1652745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MC112"],"award-info":[{"award-number":["ZR2020MC112"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MC112"],"award-info":[{"award-number":["ZR2020MC112"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MC112"],"award-info":[{"award-number":["ZR2020MC112"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MC112"],"award-info":[{"award-number":["ZR2020MC112"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>The primary determinant of crop yield is photosynthetic capacity, which is under the control of photosynthesis-related genes. Therefore, the mining of genes involved in photosynthesis is important for the study of photosynthesis. MapMan Mercator 4 is a powerful annotation tool for assigning genes into proper functional categories; however, in maize, the functions of approximately 22.15% (9520) of genes remain unclear and are labeled \u201cnot assigned\u201d, which may include photosynthesis-related genes that have not yet been identified. The fast-increasing usage of the machine learning approach in solving biological problems provides us with a new chance to identify novel photosynthetic genes from functional \u201cnot assigned\u201d genes in maize.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this study, we proved the ensemble learning model using a voting eliminates the preferences of single machine learning models. Based on this evaluation, we implemented an ensemble based ML(Machine Learning) methods using a majority voting scheme and observed that including RNA-seq data from multiple photosynthetic mutants rather than only a single mutant could increase prediction accuracy. And we call this approach \u201cA Machine Learning-based Photosynthetic-related Gene Detection approach (PGD)\u201d. Finally, we predicted 716 photosynthesis-related genes from the \u201cnot assigned\u201d category of maize MapMan annotation. The protein localization prediction (TargetP) and expression trends of these genes from maize leaf sections indicated that the prediction was reliable and robust. And we put this approach online base on google colab.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>This study reveals a new approach for mining novel genes related to a specific functional category and provides candidate genes for researchers to experimentally define their biological functions.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-022-04722-x","type":"journal-article","created":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T11:03:01Z","timestamp":1652785381000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["PGD: a machine learning-based photosynthetic-related gene detection approach"],"prefix":"10.1186","volume":"23","author":[{"given":"Yunchuan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuru","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daohong","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pinghua","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baijuan","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"4722_CR1","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1111\/j.1365-313X.2004.02016.x","volume":"37","author":"O Thimm","year":"2004","unstructured":"Thimm O, Bl\u00e4sing O, Gibon Y, Nagel A, Meyer S, Kr\u00fcger P, et al. mapman: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004;37:914\u201339.","journal-title":"Plant J"},{"key":"4722_CR2","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/978-1-59745-515-2_5","volume":"396","author":"N Mulder","year":"2007","unstructured":"Mulder N, Apweiler R. InterPro and InterProScan: tools for protein sequence classification and comparison. Methods Mol Biol. 2007;396:59\u201370.","journal-title":"Methods Mol Biol"},{"key":"4722_CR3","doi-asserted-by":"crossref","unstructured":"Marchler-Bauer A, Zheng C, Chitsaz F, Derbyshire MK, Geer LY, Geer RC, et al. CDD: conserved domains and protein three-dimensional structure. Nucleic Acids Res. 2013;41 Database issue:D348\u2013352.","DOI":"10.1093\/nar\/gks1243"},{"key":"4722_CR4","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","volume":"596","author":"J Jumper","year":"2021","unstructured":"Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583\u20139.","journal-title":"Nature"},{"key":"4722_CR5","doi-asserted-by":"crossref","unstructured":"Dai X, Xu Z, Liang Z, Tu X, Zhong S, Schnable JC, et al. Non\u2010homology\u2010based prediction of gene functions in maize (Zea mays ssp. mays ). Plant Genome. 2020;13.","DOI":"10.1002\/tpg2.20015"},{"key":"4722_CR6","doi-asserted-by":"crossref","unstructured":"Lambers H, Chapin FS, Pons TL. Photosynthesis. In: Plant physiological ecology. New York, NY: Springer New York; 2008. p. 11\u201399.","DOI":"10.1007\/978-0-387-78341-3_2"},{"key":"4722_CR7","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1093\/pcp\/pcy108","volume":"59","author":"N Chen","year":"2018","unstructured":"Chen N, Wang P, Li C, Wang Q, Pan J, Xiao F, et al. A single nucleotide mutation of the IspE gene participating in the MEP pathway for isoprenoid biosynthesis causes a green-revertible yellow leaf phenotype in rice. Plant Cell Physiol. 2018;59:1905\u201317.","journal-title":"Plant Cell Physiol"},{"key":"4722_CR8","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","volume":"15","author":"MI Love","year":"2014","unstructured":"Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.","journal-title":"Genome Biol"},{"key":"4722_CR9","unstructured":"Dorogush AV, Ershov V, Gulin A. CatBoost: gradient boosting with categorical features support. arXiv:181011363 [cs, stat]. 2018."},{"key":"4722_CR10","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, et al. LightGBM: a highly efficient gradient boosting decision tree 9."},{"key":"4722_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:5\u201332.","journal-title":"Mach Learn"},{"key":"4722_CR12","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost. A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco California USA: ACM; 2016. p. 785\u201394.","DOI":"10.1145\/2939672.2939785"},{"key":"4722_CR13","doi-asserted-by":"publisher","first-page":"e62","DOI":"10.1093\/nar\/gkaa264","volume":"48","author":"Q Song","year":"2020","unstructured":"Song Q, Lee J, Akter S, Rogers M, Grene R, Li S. Prediction of condition-specific regulatory genes using machine learning. Nucleic Acids Res. 2020;48:e62\u2013e62.","journal-title":"Nucleic Acids Res"},{"key":"4722_CR14","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1038\/nbt.3019","volume":"32","author":"L Wang","year":"2014","unstructured":"Wang L, Czedik-Eysenberg A, Mertz RA, Si Y, Tohge T, Nunes-Nesi A, et al. Comparative analyses of C4 and C3 photosynthesis in developing leaves of maize and rice. Nat Biotechnol. 2014;32:1158\u201365.","journal-title":"Nat Biotechnol"},{"key":"4722_CR15","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1007\/s00709-016-1010-y","volume":"254","author":"X Qi","year":"2017","unstructured":"Qi X, Xu W, Zhang J, Guo R, Zhao M, Hu L, et al. Physiological characteristics and metabolomics of transgenic wheat containing the maize C4 phosphoenolpyruvate carboxylase (PEPC) gene under high temperature stress. Protoplasma. 2017;254:1017\u201330.","journal-title":"Protoplasma"},{"key":"4722_CR16","unstructured":"Bergantino E, Sandona D, Cugini D, Bassi R. The photosystem II subunit CP29 can be phosphorylated in both C3 and C4 plants as suggested by sequence analysis 12."},{"key":"4722_CR17","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s11120-019-00655-4","volume":"142","author":"R Giuliani","year":"2019","unstructured":"Giuliani R, Karki S, Covshoff S, Lin H-C, Coe RA, Koteyeva NK, et al. Transgenic maize phosphoenolpyruvate carboxylase alters leaf\u2013atmosphere CO2 and 13CO2 exchanges in Oryza sativa. Photosynth Res. 2019;142:153\u201367.","journal-title":"Photosynth Res"},{"key":"4722_CR18","doi-asserted-by":"publisher","first-page":"2621","DOI":"10.1093\/emboj\/18.9.2621","volume":"18","author":"DG Fisk","year":"1999","unstructured":"Fisk DG, Walker MB, Barkan A. Molecular cloning of the maize gene crp1 reveals similarity between regulators of mitochondrial and chloroplast gene expression. EMBO J. 1999;18:2621\u201330.","journal-title":"EMBO J"},{"key":"4722_CR19","doi-asserted-by":"crossref","unstructured":"Almagro Armenteros JJ, Salvatore M, Emanuelsson O, Winther O, von Heijne G, Elofsson A, et al. Detecting novel sequence signals in targeting peptides using deep learning. preprint. Bioinformatics;2019.","DOI":"10.1101\/639203"},{"key":"4722_CR20","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.1111\/tpj.14799","volume":"103","author":"JA Fernandez-Gallego","year":"2020","unstructured":"Fernandez-Gallego JA, Lootens P, Borra-Serrano I, Derycke V, Haesaert G, Rold\u00e1n-Ruiz I, et al. Automatic wheat ear counting using machine learning based on RGB UAV imagery. Plant J. 2020;103:1603\u201313.","journal-title":"Plant J"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04722-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-022-04722-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-022-04722-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T11:03:48Z","timestamp":1652785428000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-022-04722-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,17]]},"references-count":20,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["4722"],"URL":"https:\/\/doi.org\/10.1186\/s12859-022-04722-x","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,17]]},"assertion":[{"value":"17 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"183"}}