Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Frontiers in plant science Ročník 13; s. 831204
Hlavní autori:	Su, Lingtao, Xu, Chunhui, Zeng, Shuai, Su, Li, Joshi, Trupti, Stacey, Gary, Xu, Dong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Media SA 03.03.2022 Frontiers Media S.A
Predmet:	autoencoder Biological effects Data compression Datasets deep learning Gene expression gene regulatory network Genes Genomes Heterogeneity Modules Plant Science Plant tissues Source code soybean Soybeans Statistical power tissue-specific gene Transcription factors transcriptome analysis Transcriptomes functional module soybean deep learning autoencoder transcriptome analysis gene regulatory network tissue-specific gene
ISSN:	1664-462X, 1664-462X
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Abstract	Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta .
AbstractList	Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta. Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta. Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta . Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source athttps://github.com/LingtaoSu/SoyMeta.
Author	Zeng, Shuai Su, Lingtao Su, Li Joshi, Trupti Xu, Chunhui Stacey, Gary Xu, Dong
AuthorAffiliation	4 Division of Plant Sciences and Technology and Biochemistry Christopher S. Bond Life Sciences Center, University of Missouri , Columbia, MO , United States 1 Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri , Columbia, MO , United States 3 Department of Health Management and Informatics and Christopher S. Bond Life Sciences Center, University of Missouri , Columbia, MO , United States 2 Institute for Data Science and Informatics, Christopher S. Bond Life Sciences Center, University of Missouri , Columbia, MO , United States
AuthorAffiliation_xml	– name: 1 Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri , Columbia, MO , United States – name: 2 Institute for Data Science and Informatics, Christopher S. Bond Life Sciences Center, University of Missouri , Columbia, MO , United States – name: 4 Division of Plant Sciences and Technology and Biochemistry Christopher S. Bond Life Sciences Center, University of Missouri , Columbia, MO , United States – name: 3 Department of Health Management and Informatics and Christopher S. Bond Life Sciences Center, University of Missouri , Columbia, MO , United States
Author_xml	– sequence: 1 givenname: Lingtao surname: Su fullname: Su, Lingtao – sequence: 2 givenname: Chunhui surname: Xu fullname: Xu, Chunhui – sequence: 3 givenname: Shuai surname: Zeng fullname: Zeng, Shuai – sequence: 4 givenname: Li surname: Su fullname: Su, Li – sequence: 5 givenname: Trupti surname: Joshi fullname: Joshi, Trupti – sequence: 6 givenname: Gary surname: Stacey fullname: Stacey, Gary – sequence: 7 givenname: Dong surname: Xu fullname: Xu, Dong
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/35310659$$D View this record in MEDLINE/PubMed
BookMark	eNp1kk1rGzEQhpeS0qRp7j2VhV56sauVdvVxKYTQD4NLD4mhNzGWRq7MWnKlXYP_fbV1GpJAdZDE6J2HGc37ujoLMWBVvW3InDGpPrp9n-eUUDqXrKGkfVFdNJy3s5bTn2eP7ufVVc5bUlZHiFLiVXXOOtYQ3qmLCpaQNji7NdBjvQgDbhIM_oD1dYD-mH2uo6tv43GNEOq7BCGb5PdD3GG9yj5s6hJehTzuMR18Rltfj0PEYKLFVH8ve_-meumgz3h1f15Wqy-f726-zZY_vi5urpcz0yoyzLhoO8tb5xSz1lKUljjpwEiq7NooJ4QwwnAlFaAzTQtmaoGvAQV0xgG7rBYnro2w1fvkd5COOoLXfwMxbTSkwZseNRjHrWiENQWEgkHHhOwYt6oxxlBVWJ9OrP243qE1GIYE_RPo05fgf-lNPGipqFCSFcCHe0CKv0fMg975bLDvIWAcs6a8bbpGEtoU6ftn0m0cU_n9rBkVTHay45Pq3eOKHkr5N8kiICeBSTHnhO5B0hA9-UVPftGTX_TJLyWFP0sxfijTj1NPvv9_4h_54MeY
CitedBy_id	crossref_primary_10_1016_j_envexpbot_2024_105988 crossref_primary_10_1002_tpg2_20516 crossref_primary_10_1186_s13059_024_03393_6 crossref_primary_10_1016_j_cpb_2025_100523 crossref_primary_10_1186_s40537_025_01078_w crossref_primary_10_3390_ijms241713072
Cites_doi	10.1186/s12859-019-2952-9 10.1105/tpc.18.00961 10.1111/j.1469-8137.2006.01901.x 10.1186/s12870-019-2132-0 10.1074/jbc.M210538200 10.1093/bioinformatics/btg405 10.1105/tpc.20.00080 10.1093/nar/gkt905 10.1371/journal.pone.0059270 10.1186/1471-2105-9-271 10.1186/1471-2229-10-160 10.15252/msb.20188591 10.1186/s12918-016-0349-1 10.1038/Nmeth.2016 10.12688/f1000research.15931.2 10.1093/nar/gkx681 10.1093/bioinformatics/bts635 10.1111/nph.13365 10.1038/nature02039 10.1186/1755-8794-1-42 10.1093/nar/gkw704 10.1104/pp.68.4.840 10.3389/fpls.2020.604690 10.1371/journal.pone.0062288 10.1186/s12864-019-5434-6 10.1104/pp.108.118141 10.1111/nph.14992 10.1093/biostatistics/kxj037 10.3390/ijms21207603 10.1038/s41467-018-04368-5 10.1093/bib/bbs037 10.1101/gad.14.10.1269 10.1007/s004250050675 10.1105/tpc.113.114017 10.1104/pp.109.144030 10.1093/database/baaa038 10.1104/pp.120.3.867 10.3389/fpls.2019.00578 10.1038/nature12831 10.1093/bioinformatics/btm254 10.1103/PhysRevE.70.066111 10.1093/nar/gkt338 10.1093/nar/gkl595 10.1016/j.plaphy.2011.12.007 10.1002/pld3.167 10.1093/biostatistics/4.2.249 10.1186/1471-2164-13-S1-S15 10.1371/journal.pone.0012776 10.4238/2015.December.28.39 10.1093/nar/gkaa1107 10.1111/nph.15845 10.1038/s41598-017-18235-8 10.1093/bioinformatics/bti042 10.1111/j.1365-313X.2010.04222.x 10.1038/s41467-020-15851-3 10.1093/bioinformatics/btq431 10.1016/j.omtn.2018.03.001 10.1007/s12064-012-0162-3 10.1186/s12864-017-4226-0 10.1038/s41467-017-02285-7 10.1093/bioinformatics/btg385 10.1111/j.2517-6161.1995.tb02031.x 10.1186/s12864-018-5370-x 10.1104/pp.16.01254 10.1104/pp.17.01086 10.1179/174329211x13020951739811 10.1038/ncomms2621 10.1104/pp.16.01610 10.1111/tpj.14850 10.1007/s40484-018-0144-7 10.1126/science.aag1125 10.1007/s00425-007-0680-2 10.1186/1471-2105-9-452 10.1105/tpc.108.059857 10.1038/s41598-020-58546-x 10.1105/tpc.112.095984 10.1002/j.1460-2075.1984.tb02033.x 10.1111/pce.13175 10.1186/s12870-018-1329-y 10.1093/bioinformatics/btq109 10.1093/bioinformatics/btaa796 10.1093/bioinformatics/btl140 10.3389/fpls.2018.01792 10.1093/nar/gky964 10.1007/s10888-011-9188-x 10.1016/j.celrep.2017.10.001 10.1093/nar/gkw982 10.1093/nar/gkp798 10.1105/tpc.108.061010 10.1038/srep42248 10.1093/nar/gkq1019 10.3389/fpls.2015.00578 10.3389/fpls.2017.00567 10.1093/nargab/lqaa078 10.1105/tpc.19.00279 10.1186/s12870-015-0553-y 10.3389/fpls.2017.01030 10.1038/tpj.2010.57 10.1007/978-1-4939-6658-5_7 10.1104/pp.108.123422 10.1109/BIBM.2015.7359871
ContentType	Journal Article
Copyright	Copyright © 2022 Su, Xu, Zeng, Su, Joshi, Stacey and Xu. 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2022 Su, Xu, Zeng, Su, Joshi, Stacey and Xu. 2022 Su, Xu, Zeng, Su, Joshi, Stacey and Xu
Copyright_xml	– notice: Copyright © 2022 Su, Xu, Zeng, Su, Joshi, Stacey and Xu. – notice: 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Copyright © 2022 Su, Xu, Zeng, Su, Joshi, Stacey and Xu. 2022 Su, Xu, Zeng, Su, Joshi, Stacey and Xu
DBID	AAYXX CITATION NPM 3V. 7X2 8FE 8FH 8FK ABUWG AEUYN AFKRA ATCPS AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M0K M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM DOA
DOI	10.3389/fpls.2022.831204
DatabaseName	CrossRef PubMed ProQuest Central (Corporate) Agricultural Science Collection ProQuest SciTech Collection ProQuest Natural Science Collection ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability (subscription) ProQuest Central UK/Ireland Agricultural & Environmental Science Database ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Biological Science Collection Agricultural Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef PubMed Agricultural Science Database Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability Natural Science Collection ProQuest Central Korea Agricultural & Environmental Science Collection Biological Science Collection ProQuest Central (New) ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Agricultural Science Collection Biological Science Database ProQuest SciTech Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic CrossRef Agricultural Science Database PubMed
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: ProQuest Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Botany
EISSN	1664-462X
ExternalDocumentID	oai_doaj_org_article_acf6d717dcc14e73a5378536d91ccc29 PMC8927983 35310659 10_3389_fpls_2022_831204
Genre	Journal Article
GrantInformation_xml	– fundername: NIGMS NIH HHS grantid: R35 GM126985 – fundername: ;
GroupedDBID	5VS 9T4 AAFWJ AAKDD AAYXX ACGFO ACGFS ADBBV ADRAZ AENEX AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS BCNDV CITATION EBD ECGQY GROUPED_DOAJ GX1 HYE KQ8 M48 M~E OK1 PGMZT RNS RPM ACXDI IAO IEA IGS IPNFZ ISR NPM RIG 3V. 7X2 8FE 8FH 8FK ABUWG AEUYN AFKRA ATCPS AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M0K M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM
ID	FETCH-LOGICAL-c490t-6745d64ff93ddd2e8d0f8fac829dbc9f777c7c6989aefc14ac31066bae7a5cfa3
IEDL.DBID	M0K
ISICitedReferencesCount	6
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000779015000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1664-462X
IngestDate	Fri Oct 03 12:53:19 EDT 2025 Tue Sep 30 15:57:33 EDT 2025 Fri Sep 05 09:10:24 EDT 2025 Fri Nov 21 23:04:50 EST 2025 Thu Jan 02 22:54:08 EST 2025 Sat Nov 29 05:58:05 EST 2025 Tue Nov 18 21:23:05 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	functional module soybean deep learning autoencoder transcriptome analysis gene regulatory network tissue-specific gene
Language	English
License	Copyright © 2022 Su, Xu, Zeng, Su, Joshi, Stacey and Xu. This is an open-access article distributed under the terms of the Creative 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.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c490t-6745d64ff93ddd2e8d0f8fac829dbc9f777c7c6989aefc14ac31066bae7a5cfa3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Reviewed by: Song Li, Virginia Tech, United States; John Louis Van Hemert, Corteva Agriscience™, United States This article was submitted to Plant Bioinformatics, a section of the journal Frontiers in Plant Science Edited by: Xiyin Wang, Agricultural University of Hebei, China
OpenAccessLink	https://www.proquest.com/docview/3273858561?pq-origsite=%requestingapplication%
PMID	35310659
PQID	3273858561
PQPubID	7426805
ParticipantIDs	doaj_primary_oai_doaj_org_article_acf6d717dcc14e73a5378536d91ccc29 pubmedcentral_primary_oai_pubmedcentral_nih_gov_8927983 proquest_miscellaneous_2641518021 proquest_journals_3273858561 pubmed_primary_35310659 crossref_primary_10_3389_fpls_2022_831204 crossref_citationtrail_10_3389_fpls_2022_831204
PublicationCentury	2000
PublicationDate	2022-03-03
PublicationDateYYYYMMDD	2022-03-03
PublicationDate_xml	– month: 03 year: 2022 text: 2022-03-03 day: 03
PublicationDecade	2020
PublicationPlace	Switzerland
PublicationPlace_xml	– name: Switzerland – name: Lausanne
PublicationTitle	Frontiers in plant science
PublicationTitleAlternate	Front Plant Sci
PublicationYear	2022
Publisher	Frontiers Media SA Frontiers Media S.A
Publisher_xml	– name: Frontiers Media SA – name: Frontiers Media S.A
References	Lara (B46) 2003; 278 Lohar (B58) 2007; 173 Marbach (B62) 2012; 9 Haibe-Kains (B32) 2013; 504 Lin (B55) 2017; 45 Brown (B11) 2021; 49 Cortijo (B16) 2019; 15 Gordon (B27) 1999; 120 Benjamini (B7) 1995; 57 Gupta (B30) 2015 Kim (B43) 2008; 227 Moisseyev (B65) 2020; 2020 Bolle (B9) 2000; 14 Sonawane (B77) 2017; 21 Zilli (B101) 2011; 16 Joshi (B41) 2017; 1533 Gupta (B31) 2017; 8 Joshi (B39) 2014; 42 Marcker (B63) 1984; 3 Konishi (B45) 2013; 4 Xie (B91) 2017; 18 Wang (B84) 2019; 3 Dobin (B20) 2013; 29 Wu (B88) 2019; 223 Streeter (B78) 1981; 68 Luo (B59) 2010; 10 Yi (B96) 2018; 11 Yan (B92) 2015; 207 Li (B50) 2017; 173 Xiao (B90) 2010; 26 Sun (B79) 2020; 21 Wang (B86) 2019; 20 Huynh-Thu (B34) 2010; 5 Senovilla (B73) 2018; 218 Lv (B60) 2017; 8 Jones (B38) 2013; 8 Du (B21) 2019; 20 Gautier (B25) 2004; 20 Clauset (B15) 2004; 70 Gharaibeh (B26) 2008; 9 Zhou (B100) 2020; 32 Chen (B14) 2012; 24 Lazar (B47) 2013; 14 Sinharoy (B76) 2013; 25 Ezer (B23) 2017; 175 Seabra (B71) 2015; 6 Irizarry (B35) 2003; 4 Kinalis (B44) 2019; 20 Elhady (B22) 2020; 10 Kim (B42) 2017; 45 Wingett (B87) 2018; 7 Qi (B68) 2018; 41 Pucciariello (B67) 2019; 10 Huang (B33) 2018; 18 Yi (B95) 2019; 31 Liu (B57) 2015; 14 Yang (B94) 2017; 7 Araujo (B2) 2017; 8 Li (B52) 2020; 11 Liu (B56) 2008; 9 Yuan (B98) 2017; 7 Johnson (B37) 2007; 8 Guenther (B29) 2000; 210 Vernie (B81) 2008; 20 Machado (B61) 2020; 103 Libault (B53) 2010; 63 Li (B51) 2018; 6 Sims (B75) 2008; 1 Fan (B24) 2013; 8 Walley (B83) 2016; 353 Wagner (B82) 2012; 131 Asamizu (B4) 2008; 147 Carvalho (B12) 2010; 26 Asakura (B3) 2012; 52 Brown (B10) 2015; 15 Alexa (B1) 2006; 22 Jin (B36) 2017; 45 Xia (B89) 2013; 41 Radutoiu (B69) 2003; 425 Leinonen (B48) 2011; 39 Roy (B70) 2020; 32 Mckenzie (B64) 2016; 10 Ceriani (B13) 2012; 10 Libault (B54) 2009; 151 Yanai (B93) 2005; 21 Zhang (B99) 2020; 2 Wang (B85) 2020; 11 Ding (B19) 2018; 9 Van Heerden (B80) 2008; 148 Berkowitz (B8) 2008; 20 Athar (B5) 2019; 47 Cui (B17) 2019; 19 Yu (B97) 2006; 34 Joshi (B40) 2012 Grant (B28) 2010; 38 Benito (B6) 2004; 20 Sean (B72) 2007; 23 Li (B49) 2018; 9 Nagae (B66) 2016; 172 Dincer (B18) 2020; 36 Severin (B74) 2010; 10
References_xml	– volume: 20 start-page: 379 year: 2019 ident: B44 article-title: Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data. publication-title: BMC Bioinformatics doi: 10.1186/s12859-019-2952-9 – volume: 31 start-page: 974 year: 2019 ident: B95 article-title: High temporal-resolution transcriptome landscape of early maize seed development. publication-title: Plant Cell doi: 10.1105/tpc.18.00961 – volume: 173 start-page: 39 year: 2007 ident: B58 article-title: A transient decrease in reactive oxygen species in roots leads to root hair deformation in the legume-rhizobia symbiosis publication-title: New Phytol. doi: 10.1111/j.1469-8137.2006.01901.x – volume: 19 start-page: 598 year: 2019 ident: B17 article-title: GmWRKY40, a member of the WRKY transcription factor genes identified from Glycine max L., enhanced the resistance to Phytophthora sojae. publication-title: BMC Plant Biol. doi: 10.1186/s12870-019-2132-0 – volume: 278 start-page: 21003 year: 2003 ident: B46 article-title: Synergistic activation of seed storage protein gene expression in Arabidopsis by ABI3 and two bZIPs related to OPAQUE2. publication-title: J. Biol. Chem. doi: 10.1074/jbc.M210538200 – volume: 20 start-page: 307 year: 2004 ident: B25 article-title: AFFY – analysis of Affymetrix GeneChip data at the probe level. publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg405 – volume: 32 start-page: 1377 year: 2020 ident: B100 article-title: Meta gene regulatory networks in maize highlight functionally relevant regulatory interactions. publication-title: Plant Cell doi: 10.1105/tpc.20.00080 – volume: 42 start-page: D1245 year: 2014 ident: B39 article-title: Soybean knowledge base (SoyKB): a web resource for integration of soybean translational genomics and molecular breeding. publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkt905 – volume: 8 start-page: e59270 year: 2013 ident: B38 article-title: Using RNA-Seq to profile soybean seed development from fertilization to maturity. publication-title: PLoS One doi: 10.1371/journal.pone.0059270 – volume: 9 start-page: 271 year: 2008 ident: B56 article-title: TiGER: A database for tissue-specific gene expression and regulation. publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-271 – volume: 10 start-page: 160 year: 2010 ident: B74 article-title: RNA-Seq atlas of glycine max: a guide to the soybean transcriptome. publication-title: BMC Plant Biol. doi: 10.1186/1471-2229-10-160 – volume: 15 start-page: e8591 year: 2019 ident: B16 article-title: Widespread inter-individual gene expression variability in Arabidopsis thaliana. publication-title: Mol. Syst. Biol. doi: 10.15252/msb.20188591 – volume: 10 start-page: 106 year: 2016 ident: B64 article-title: DGCA: A comprehensive R package for differential gene correlation analysis. publication-title: BMC Syst. Biol. doi: 10.1186/s12918-016-0349-1 – volume: 9 start-page: 796 year: 2012 ident: B62 article-title: Wisdom of crowds for robust gene network inference. publication-title: Nat. Methods doi: 10.1038/Nmeth.2016 – volume: 7 start-page: 1338 year: 2018 ident: B87 article-title: FastQ screen: a tool for multi-genome mapping and quality control. publication-title: F1000Res doi: 10.12688/f1000research.15931.2 – volume: 45 start-page: e156 year: 2017 ident: B55 article-title: Using neural networks for reducing the dimensions of single-cell RNA-Seq data. publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkx681 – volume: 29 start-page: 15 year: 2013 ident: B20 article-title: STAR: ultrafast universal RNA-seq aligner. publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts635 – volume: 207 start-page: 748 year: 2015 ident: B92 article-title: Identification of microRNAs and their mRNA targets during soybean nodule development: functional analysis of the role of miR393j-3p in soybean nodulation. publication-title: New Phytol. doi: 10.1111/nph.13365 – volume: 425 start-page: 585 year: 2003 ident: B69 article-title: Plant recognition of symbiotic bacteria requires two LysM receptor-like kinases. publication-title: Nature doi: 10.1038/nature02039 – volume: 1 start-page: 42 year: 2008 ident: B75 article-title: The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets - improving meta-analysis and prediction of prognosis. publication-title: BMC Med. Genomics doi: 10.1186/1755-8794-1-42 – volume: 45 start-page: D1082 year: 2017 ident: B42 article-title: SoyNet: a database of co-functional networks for soybean Glycine max. publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkw704 – volume: 68 start-page: 840 year: 1981 ident: B78 article-title: Effect of nitrate in the rooting medium on carbohydrate composition of soybean nodules. publication-title: Plant Physiol. doi: 10.1104/pp.68.4.840 – volume: 11 start-page: 604690 year: 2020 ident: B85 article-title: Genome-wide analysis of the GRAS gene family and functional identification of GmGRAS37 in drought and salt tolerance. publication-title: Front. Plant Sci. doi: 10.3389/fpls.2020.604690 – volume: 8 start-page: e62288 year: 2013 ident: B24 article-title: Genome-wide expression analysis of soybean MADS genes showing potential function in the seed development. publication-title: PLoS One doi: 10.1371/journal.pone.0062288 – volume: 20 start-page: 51 year: 2019 ident: B86 article-title: Genome-wide systematic characterization of bZIP transcription factors and their expression profiles during seed development and in response to salt stress in peanut. publication-title: BMC Genomics doi: 10.1186/s12864-019-5434-6 – volume: 147 start-page: 2030 year: 2008 ident: B4 article-title: A positive regulatory role for LjERF1 in the nodulation process is revealed by systematic analysis of nodule-associated transcription factors of Lotus japonicus. publication-title: Plant Physiol. doi: 10.1104/pp.108.118141 – volume: 218 start-page: 696 year: 2018 ident: B73 article-title: Medicago truncatula copper transporter 1 (MtCOPT1) delivers copper for symbiotic nitrogen fixation. publication-title: New Phytol. doi: 10.1111/nph.14992 – volume: 8 start-page: 118 year: 2007 ident: B37 article-title: Adjusting batch effects in microarray expression data using empirical Bayes methods. publication-title: Biostatistics doi: 10.1093/biostatistics/kxj037 – volume: 21 start-page: 7603 year: 2020 ident: B79 article-title: Analysis of spatio-temporal transcriptome profiles of soybean (Glycine max) tissues during early seed development. publication-title: Int. J. Mol. Sci. doi: 10.3390/ijms21207603 – volume: 9 start-page: 2002 year: 2018 ident: B19 article-title: Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. publication-title: Nat. Commun. doi: 10.1038/s41467-018-04368-5 – volume: 14 start-page: 469 year: 2013 ident: B47 article-title: Batch effect removal methods for microarray gene expression data integration: a survey. publication-title: Brief Bioinform. doi: 10.1093/bib/bbs037 – volume: 14 start-page: 1269 year: 2000 ident: B9 article-title: PAT1, a new member of the GRAS family, is involved in phytochrome A signal transduction. publication-title: Genes Dev. doi: 10.1101/gad.14.10.1269 – volume: 210 start-page: 741 year: 2000 ident: B29 article-title: Water-selective and multifunctional aquaporins from Lotus japonicus nodules. publication-title: Planta doi: 10.1007/s004250050675 – volume: 25 start-page: 3584 year: 2013 ident: B76 article-title: The C2H2 transcription factor regulator of symbiosome differentiation represses transcription of the secretory pathway gene VAMP721a and promotes symbiosome development in Medicago truncatula. publication-title: Plant Cell doi: 10.1105/tpc.113.114017 – volume: 151 start-page: 1207 year: 2009 ident: B54 article-title: Large-scale analysis of putative soybean regulatory gene expression identifies a Myb gene involved in soybean nodule development. publication-title: Plant Physiol. doi: 10.1104/pp.109.144030 – volume: 2020 start-page: baaa038 year: 2020 ident: B65 article-title: RGPDB: database of root-associated genes and promoters in maize, soybean, and sorghum. publication-title: Database (Oxford) doi: 10.1093/database/baaa038 – volume: 120 start-page: 867 year: 1999 ident: B27 article-title: Sucrose synthase in legume nodules is essential for nitrogen fixation. publication-title: Plant Physiol. doi: 10.1104/pp.120.3.867 – volume: 10 start-page: 578 year: 2019 ident: B67 article-title: Exploring legume-rhizobia symbiotic models for waterlogging tolerance. publication-title: Front. Plant Sci. doi: 10.3389/fpls.2019.00578 – volume: 504 start-page: 389 year: 2013 ident: B32 article-title: Inconsistency in large pharmacogenomic studies. publication-title: Nature doi: 10.1038/nature12831 – volume: 23 start-page: 1846 year: 2007 ident: B72 article-title: GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor. publication-title: Bioinformatics doi: 10.1093/bioinformatics/btm254 – volume: 70 start-page: 06611 year: 2004 ident: B15 article-title: Finding community structure in very large networks. publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.70.066111 – volume: 41 start-page: W63 year: 2013 ident: B89 article-title: INMEX–a web-based tool for integrative meta-analysis of expression data. publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkt338 – volume: 34 start-page: 4925 year: 2006 ident: B97 article-title: Computational analysis of tissue-specific combinatorial gene regulation: predicting interaction between transcription factors in human tissues. publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkl595 – volume: 52 start-page: 147 year: 2012 ident: B3 article-title: Global gene expression profiles in developing soybean seeds. publication-title: Plant Physiol. Biochem. doi: 10.1016/j.plaphy.2011.12.007 – volume: 3 start-page: e00167 year: 2019 ident: B84 article-title: SoyCSN: Soybean context-specific network analysis and prediction based on tissue-specific transcriptome data. publication-title: Plant Direct doi: 10.1002/pld3.167 – volume: 4 start-page: 249 year: 2003 ident: B35 article-title: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. publication-title: Biostatistics doi: 10.1093/biostatistics/4.2.249 – start-page: S15 year: 2012 ident: B40 article-title: Soybean Knowledge Base (SoyKB): a web resource for soybean translational genomics. publication-title: BMC Genomics doi: 10.1186/1471-2164-13-S1-S15 – volume: 5 start-page: e12776 year: 2010 ident: B34 article-title: Inferring regulatory networks from expression data using tree-based methods. publication-title: PLoS One doi: 10.1371/journal.pone.0012776 – volume: 14 start-page: 18895 year: 2015 ident: B57 article-title: Identification of genes associated with the increased number of four-seed pods in soybean (Glycine max L.) using transcriptome analysis. publication-title: Genet. Mol. Res. doi: 10.4238/2015.December.28.39 – volume: 49 start-page: D1496 year: 2021 ident: B11 article-title: A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database. publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkaa1107 – volume: 223 start-page: 2104 year: 2019 ident: B88 article-title: A global coexpression network of soybean genes gives insights into the evolution of nodulation in nonlegumes and legumes. publication-title: New Phytol. doi: 10.1111/nph.15845 – volume: 7 start-page: 17804 year: 2017 ident: B94 article-title: Characterization of soybean WRKY gene family and identification of soybean WRKY genes that promote resistance to soybean cyst nematode. publication-title: Sci. Rep. doi: 10.1038/s41598-017-18235-8 – volume: 21 start-page: 650 year: 2005 ident: B93 article-title: Genome-wide midrange transcription profiles reveal expression level relationships in human tissue specification. publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti042 – volume: 63 start-page: 86 year: 2010 ident: B53 article-title: An integrated transcriptome atlas of the crop model Glycine max, and its use in comparative analyses in plants. publication-title: Plant J. doi: 10.1111/j.1365-313X.2010.04222.x – volume: 11 start-page: 2338 year: 2020 ident: B52 article-title: Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis. publication-title: Nat. Commun. doi: 10.1038/s41467-020-15851-3 – volume: 26 start-page: 2363 year: 2010 ident: B12 article-title: A framework for oligonucleotide microarray preprocessing. publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq431 – volume: 11 start-page: 337 year: 2018 ident: B96 article-title: A deep learning framework for robust and accurate prediction of ncRNA-protein interactions using evolutionary information. publication-title: Mol. Therapy Nucleic Acids doi: 10.1016/j.omtn.2018.03.001 – volume: 131 start-page: 281 year: 2012 ident: B82 article-title: Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. publication-title: Theory Biosci. doi: 10.1007/s12064-012-0162-3 – volume: 18 start-page: 845 year: 2017 ident: B91 article-title: A deep auto-encoder model for gene expression prediction. publication-title: BMC Genomics doi: 10.1186/s12864-017-4226-0 – volume: 8 start-page: 2132 year: 2017 ident: B2 article-title: Stochastic gene expression in Arabidopsis thaliana. publication-title: Nat. Commun. doi: 10.1038/s41467-017-02285-7 – volume: 20 start-page: 105 year: 2004 ident: B6 article-title: Adjustment of systematic microarray data biases. publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg385 – volume: 57 start-page: 289 year: 1995 ident: B7 article-title: Controlling the false discovery rate - a practical and powerful approach to multiple testing. publication-title: J. R. Stat. Soc. Ser. B Stat. Methodol. doi: 10.1111/j.2517-6161.1995.tb02031.x – volume: 20 start-page: 82 year: 2019 ident: B21 article-title: Gene2vec: distributed representation of genes based on co-expression. publication-title: BMC Genomics doi: 10.1186/s12864-018-5370-x – volume: 172 start-page: 2033 year: 2016 ident: B66 article-title: The thiamine biosynthesis gene THI1 promotes nodule growth and seed maturation. publication-title: Plant Physiol. doi: 10.1104/pp.16.01254 – volume: 175 start-page: 628 year: 2017 ident: B23 article-title: The G-Box transcriptional regulatory code in Arabidopsis. publication-title: Plant Physiol. doi: 10.1104/pp.17.01086 – volume: 16 start-page: 49 year: 2011 ident: B101 article-title: Symbiotic association between soybean plants and Bradyrhizobium japonicum develops oxidative stress and heme oxygenase-1 induction at early stages. publication-title: Redox Rep. doi: 10.1179/174329211x13020951739811 – volume: 4 start-page: 1617 year: 2013 ident: B45 article-title: Arabidopsis NIN-like transcription factors have a central role in nitrate signalling. publication-title: Nat. Commun. doi: 10.1038/ncomms2621 – volume: 173 start-page: 2208 year: 2017 ident: B50 article-title: Selection for a zinc-finger protein contributes to seed oil increase during soybean domestication. publication-title: Plant Physiol. doi: 10.1104/pp.16.01610 – volume: 103 start-page: 1894 year: 2020 ident: B61 article-title: Systematic analysis of 1298 RNA-Seq samples and construction of a comprehensive soybean (Glycine max) expression atlas. publication-title: Plant J. doi: 10.1111/tpj.14850 – volume: 6 start-page: 195 year: 2018 ident: B51 article-title: Modeling and analysis of RNA-seq data: a review from a statistical perspective. publication-title: Quant. Biol. doi: 10.1007/s40484-018-0144-7 – volume: 353 start-page: 814 year: 2016 ident: B83 article-title: Integration of omic networks in a developmental atlas of maize. publication-title: Science doi: 10.1126/science.aag1125 – volume: 227 start-page: 1169 year: 2008 ident: B43 article-title: Molecular characterization of a pepper C2 domain-containing SRC2 protein implicated in resistance against host and non-host pathogens and abiotic stresses. publication-title: Planta doi: 10.1007/s00425-007-0680-2 – volume: 9 start-page: 452 year: 2008 ident: B26 article-title: Background correction using dinucleotide affinities improves the performance of GCRMA. publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-9-452 – volume: 20 start-page: 2696 year: 2008 ident: B81 article-title: EFD Is an ERF transcription factor involved in the control of nodule number and differentiation in Medicago truncatula. publication-title: Plant Cell doi: 10.1105/tpc.108.059857 – volume: 10 start-page: 1619 year: 2020 ident: B22 article-title: Symbiosis of soybean with nitrogen fixing bacteria affected by root lesion nematodes in a density-dependent manner. publication-title: Sci. Rep. doi: 10.1038/s41598-020-58546-x – volume: 24 start-page: 823 year: 2012 ident: B14 article-title: A MAP kinase kinase interacts with SymRK and regulates nodule organogenesis in Lotus japonicus. publication-title: Plant Cell doi: 10.1105/tpc.112.095984 – volume: 3 start-page: 1691 year: 1984 ident: B63 article-title: Transcription of the soybean leghemoglobin genes during nodule development. publication-title: EMBO J. doi: 10.1002/j.1460-2075.1984.tb02033.x – volume: 41 start-page: 2109 year: 2018 ident: B68 article-title: Meta-analysis and transcriptome profiling reveal hub genes for soybean seed storage composition during seed development. publication-title: Plant Cell Environ. doi: 10.1111/pce.13175 – volume: 18 start-page: 111 year: 2018 ident: B33 article-title: Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize. publication-title: BMC Plant Biol. doi: 10.1186/s12870-018-1329-y – volume: 26 start-page: 1273 year: 2010 ident: B90 article-title: TiSGeD: a database for tissue-specific genes. publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq109 – volume: 36 start-page: I573 year: 2020 ident: B18 article-title: Adversarial deconfounding autoencoder for learning robust gene expression embeddings. publication-title: Bioinformatics doi: 10.1093/bioinformatics/btaa796 – volume: 22 start-page: 1600 year: 2006 ident: B1 article-title: Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl140 – volume: 9 start-page: 1792 year: 2018 ident: B49 article-title: BrLAS, a GRAS Transcription factor from brassica rapa, is involved in drought stress tolerance in transgenic Arabidopsis. publication-title: Front. Plant Sci. doi: 10.3389/fpls.2018.01792 – volume: 47 start-page: D711 year: 2019 ident: B5 article-title: ArrayExpress update – from bulk to single-cell expression data. publication-title: Nucleic Acids Res. doi: 10.1093/nar/gky964 – volume: 10 start-page: 421 year: 2012 ident: B13 article-title: The origins of the Gini index: extracts from VariabilitA e MutabilitA (1912) by Corrado Gini. publication-title: J. Econ. Inequality doi: 10.1007/s10888-011-9188-x – volume: 21 start-page: 1077 year: 2017 ident: B77 article-title: Understanding tissue-specific gene regulation. publication-title: Cell Rep. doi: 10.1016/j.celrep.2017.10.001 – volume: 45 start-page: D1040 year: 2017 ident: B36 article-title: PlantTFDB 4.0: toward a central hub for transcription factors and regulatory interactions in plants. publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkw982 – volume: 38 start-page: D843 year: 2010 ident: B28 article-title: SoyBase, the USDA-ARS soybean genetics and genomics database. publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkp798 – volume: 20 start-page: 3430 year: 2008 ident: B8 article-title: Characterization of TCTP, the translationally controlled tumor protein, from Arabidopsis thaliana. publication-title: Plant Cell doi: 10.1105/tpc.108.061010 – volume: 7 start-page: 42248 year: 2017 ident: B98 article-title: RNA-Seq analysis of nodule development at five different developmental stages of soybean (Glycine max) inoculated with Bradyrhizobium japonicum strain 113-2. publication-title: Sci. Rep. doi: 10.1038/srep42248 – volume: 39 start-page: D19 year: 2011 ident: B48 article-title: The sequence read archive. publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkq1019 – volume: 6 start-page: 578 year: 2015 ident: B71 article-title: Glutamine synthetase in Medicago truncatula, unveiling new secrets of a very old enzyme. publication-title: Front. Plant Sci. doi: 10.3389/fpls.2015.00578 – volume: 8 start-page: 567 year: 2017 ident: B31 article-title: Regulation of isoflavone biosynthesis by miRNAs in two contrasting soybean genotypes at different seed developmental stages. publication-title: Front. Plant Sci. doi: 10.3389/fpls.2017.00567 – volume: 2 start-page: lqaa078 year: 2020 ident: B99 article-title: ComBat-seq: batch effect adjustment for RNA-seq count data. publication-title: NAR Genom. Bioinform. doi: 10.1093/nargab/lqaa078 – volume: 32 start-page: 15 year: 2020 ident: B70 article-title: Celebrating 20 years of genetic discoveries in legume nodulation and symbiotic nitrogen fixation([OPEN]). publication-title: Plant Cell doi: 10.1105/tpc.19.00279 – volume: 15 start-page: 169 year: 2015 ident: B10 article-title: Developmental profiling of gene expression in soybean trifoliate leaves and cotyledons. publication-title: BMC Plant Biol. doi: 10.1186/s12870-015-0553-y – volume: 8 start-page: 1030 year: 2017 ident: B60 article-title: Molecular characterization, gene evolution, and expression analysis of the fructose-1, 6-bisphosphate Aldolase (FBA) gene family in wheat (Triticum aestivum L.). publication-title: Front. Plant Sci. doi: 10.3389/fpls.2017.01030 – volume: 10 start-page: 278 year: 2010 ident: B59 article-title: A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data. publication-title: Pharmacogenomics J. doi: 10.1038/tpj.2010.57 – volume: 1533 start-page: 149 year: 2017 ident: B41 article-title: The evolution of soybean knowledge base (SoyKB). publication-title: Methods Mol. Biol. doi: 10.1007/978-1-4939-6658-5_7 – volume: 148 start-page: 316 year: 2008 ident: B80 article-title: Regulation of respiration and the oxygen diffusion barrier in soybean protect symbiotic nitrogen fixation from chilling-induced inhibition and shoots from premature senescence. publication-title: Plant Physiol. doi: 10.1104/pp.108.123422 – start-page: 1328 year: 2015 ident: B30 article-title: Learning structure in gene expression data using deep architectures, with an application to gene clustering publication-title: Proceedings 2015 IEEE International Conference on Bioinformatics and Biomedicine doi: 10.1109/BIBM.2015.7359871
SSID	ssj0000500997
Score	2.3334584
Snippet	Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential...
SourceID	doaj pubmedcentral proquest pubmed crossref
SourceType	Open Website Open Access Repository Aggregation Database Index Database Enrichment Source
StartPage	831204
SubjectTerms	autoencoder Biological effects Data compression Datasets deep learning Gene expression gene regulatory network Genes Genomes Heterogeneity Modules Plant Science Plant tissues Source code soybean Soybeans Statistical power tissue-specific gene Transcription factors transcriptome analysis Transcriptomes
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LaxRBEG4k5OBF1PhYjdJCLh7Gneme6ccxEUOEEAIxkFvTTwysM8s-hPx7q7pnl10RvXidftDzdVVXFV39FSEnKjUq1cFWjQdtap3TlRXOVwoCMbBATieRHwpfyqsrdXenr3dKfWFOWKEHLsBNrU8iQMwRvG_aKLntuAQTI4JuvPcsP92rpd4JpgqrN7o-stxLQhSmp2k-Q3Zuxj4p3rCxLtvGDmW6_j_5mL-nSu7YnvOn5MnoNNLTsthn5FHsn5PDswEcu4cjYi8xm7u6AbQj_TrSP8AhRjeEI3RI9GZ4cNH2NNumfFIMPyLNCQMUPt_2y_Ucj41lDPR0vRqQ3zLEBcVaabMX5Pb8y7fPF9VYOaHyra5XlZBtF0SbkuYhBBZVqJNK1iumg_M6SSm99Fg70sYEuFoPXp4QzkZpO58sf0kO-qGPrwmNQcA4q5sUBczNtWMtTwyGp1o7HSdkusHR-JFWHKtbzAyEF4i8QeQNIm8K8hPycTtiXig1_tL3DLdm2w_JsPMHEBEzioj5l4hMyPFmY82ooUvDC48PuI8T8mHbDLqFFya2j8MaFiLAvUGKPOjzqsjBdiW8Q8g6mFzuScjeUvdb-vvvmb9baSa14m_-x7-9JY8RrpwVx4_JwWqxju_Iof-5ul8u3mel-AVwBBWC priority: 102 providerName: Directory of Open Access Journals
Title	Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model
URI	https://www.ncbi.nlm.nih.gov/pubmed/35310659 https://www.proquest.com/docview/3273858561 https://www.proquest.com/docview/2641518021 https://pubmed.ncbi.nlm.nih.gov/PMC8927983 https://doaj.org/article/acf6d717dcc14e73a5378536d91ccc29
Volume	13
WOSCitedRecordID	wos000779015000001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1664-462X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000500997 issn: 1664-462X databaseCode: DOA dateStart: 20100101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1664-462X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000500997 issn: 1664-462X databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Agricultural Science Database customDbUrl: eissn: 1664-462X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000500997 issn: 1664-462X databaseCode: M0K dateStart: 20110301 isFulltext: true titleUrlDefault: https://search.proquest.com/agriculturejournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1664-462X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000500997 issn: 1664-462X databaseCode: M7P dateStart: 20110301 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1664-462X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000500997 issn: 1664-462X databaseCode: BENPR dateStart: 20110301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Publicly Available Content Database customDbUrl: eissn: 1664-462X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000500997 issn: 1664-462X databaseCode: PIMPY dateStart: 20110301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Jj9MwFLaY5cCFfSkMlZG4cAhN4sTLCU1RR4yYqSKGkcopcrzASCUpTYs0F3477zlpoQjNhUsOsR295PNbbL98j5BX0ifSx1ZHiQFtyqpKRZpXJpKwEAMPVCnPw4_CZ2I6lbOZKvoNt7ZPq9zYxGCobWNwj3zEOt4VcPdvF98jrBqFp6t9CY09cgCBssKUvvP4w3aPJc4xABLd6SSsxdTIL-bI0Z2mbyRL0r4628YbBdL-f0WafydM_uGBTu7-r-z3yJ0-9qTH3WS5T265-gE5HDcQH14_JPoMk8KjCwDN0dOeRQJsId3wltDG04vmunK6psHFBYPTfHM05B1QuH1Zt-sFWp_WWXq8XjVIk2ndkmLJtfkjcnky-fTufdQXYIhMpuJVxEWWW555r5i1NnXSxl56bWSqbGWUF0IYYbAEpXbeJJk2ECxyXmkndG68Zo_Jft3U7imhznIYp1XiHYdnM1WlGfMpDPexqpQbkNEGiNL07ORYJGNewioFoSsRuhKhKzvoBuT1dsSiY-a4oe8Ysd32Q07tcKNZfil7FS218dzC6tYaeBUnmM6ZgGCGW5UYY1I1IEcbdMte0dvyN7QD8nLbDCqK5y66ds0aBOEQJSHTHvR50k2krSQsx0-Ww8PFzhTbEXW3pb76GmjApUqFkuzZzWI9J7fxQ4S0OXZE9lfLtXtBDs2P1VW7HJI9MZNDcjCeTIuPw7AhMQw6hFdR4PXnBNqL0_Pi8y-5xipi
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9QwFH4q00pwYV8GChgJDhzCJHZixweEWqDqqNPRSG2lckodL22lIRlmAc2f4jfynGVgEOqtB66Jbb04n99iP38P4HXqotSFRgWRxtUU57kMFM91kGIghhYol45XF4UHYjhMT0_laAN-tndhfFplqxMrRW1K7ffIe6zmXUFz_2HyLfBVo_zpaltCo4bFgV3-wJBt9r7_Cf_vG0r3Ph9_3A-aqgKBjmU4D7iIE8Nj5yQzxlCbmtClTumUSpNr6YQQWmhfV1FZp6NYafSAOM-VFSrRTjEc9wZsxgj2sAObo_7h6MtqVydMvMsl6vNQjP5kz03GnhWc0ncpi2hTD661f1WZgH_5tn-naP5h8_bu_G-zdRduN9412amXwz3YsMV92Not0QNePgA18GnvwRHC0pJ-w5OB2p60zCykdOSoXOZWFaQy4pVKLb9aUmVWEHx8UswWE69fZ9aQncW89ESgxk6JLyo3fggn1_J5j6BTlIV9AsQajv2UjJzlODaTOY2Zo9jdhTKXtgu99sdnuuFf92VAxhnGYR4qmYdK5qGS1VDpwttVj0nNPXJF212PpVU7zxpePSin51mjhDKlHTcYvxuNn2IFUwkT6K5xIyOtNZVd2G7RlDWqbJb9hlIXXq1eoxLyJ0uqsOUCBeHoB3ouQWzzuAbuShKW-ClLcHCxBuk1UdffFJcXFdF5KqmQKXt6tVgv4eb-8eEgG_SHB8_glp-UKkmQbUNnPl3Y57Clv88vZ9MXzWolcHbdkP8FVxaFjg
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Large-Scale+Integrative+Analysis+of+Soybean+Transcriptome+Using+an+Unsupervised+Autoencoder+Model&rft.jtitle=Frontiers+in+plant+science&rft.au=Su%2C+Lingtao&rft.au=Xu%2C+Chunhui&rft.au=Zeng%2C+Shuai&rft.au=Li%2C+Su&rft.date=2022-03-03&rft.pub=Frontiers+Media+SA&rft.eissn=1664-462X&rft.volume=13&rft.spage=831204&rft_id=info:doi/10.3389%2Ffpls.2022.831204
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1664-462X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1664-462X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1664-462X&client=summon