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For: Toro-Domínguez D, Villatoro-García JA, Martorell-Marugán J, Román-Montoya Y, Alarcón-Riquelme ME, Carmona-Sáez P. A survey of gene expression meta-analysis: methods and applications. Brief Bioinform 2021;22:1694-705. [PMID: 32095826 DOI: 10.1093/bib/bbaa019] [Cited by in Crossref: 24] [Cited by in F6Publishing: 27] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
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8 Kranz A, Polen T, Kotulla C, Arndt A, Bosco G, Bussmann M, Chattopadhyay A, Cramer A, Davoudi CF, Degner U, Diesveld R, Freiherr von Boeselager R, Gärtner K, Gätgens C, Georgi T, Geraths C, Haas S, Heyer A, Hünnefeld M, Ishige T, Kabus A, Kallscheuer N, Kever L, Klaffl S, Kleine B, Kočan M, Koch-Koerfges A, Kraxner KJ, Krug A, Krüger A, Küberl A, Labib M, Lange C, Mack C, Maeda T, Mahr R, Majda S, Michel A, Morosov X, Müller O, Nanda AM, Nickel J, Pahlke J, Pfeifer E, Platzen L, Ramp P, Rittmann D, Schaffer S, Scheele S, Spelberg S, Schulte J, Schweitzer JE, Sindelar G, Sorger-Herrmann U, Spelberg M, Stansen C, Tharmasothirajan A, Ooyen JV, van Summeren-Wesenhagen P, Vogt M, Witthoff S, Zhu L, Eikmanns BJ, Oldiges M, Schaumann G, Baumgart M, Brocker M, Eggeling L, Freudl R, Frunzke J, Marienhagen J, Wendisch VF, Bott M. A manually curated compendium of expression profiles for the microbial cell factory Corynebacterium glutamicum. Sci Data 2022;9:594. [PMID: 36182956 DOI: 10.1038/s41597-022-01706-7] [Reference Citation Analysis]
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10 Villatoro-garcía JA, Martorell-marugán J, Toro-domínguez D, Román-montoya Y, Femia P, Carmona-sáez P. DExMA: An R Package for Performing Gene Expression Meta-Analysis with Missing Genes. Mathematics 2022;10:3376. [DOI: 10.3390/math10183376] [Reference Citation Analysis]
11 Lord VO, Giudicelli GC, Recamonde-mendoza M, Vianna FSL, Kowalski TW. A transcriptome meta-analysis of ethanol embryonic exposure: Implications in neurodevelopment and neuroinflammatory genes. Neuroscience Informatics 2022;2:100094. [DOI: 10.1016/j.neuri.2022.100094] [Reference Citation Analysis]
12 Sarafidis M, Lambrou GI, Zoumpourlis V, Koutsouris D. An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer. Cancers (Basel) 2022;14:3358. [PMID: 35884419 DOI: 10.3390/cancers14143358] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
13 Kowalski TW, Lord VO, Sgarioni E, Gomes JDA, Mariath LM, Recamonde-mendoza M, Vianna FSL. Transcriptome meta-analysis of valproic acid exposure in human embryonic stem cells. European Neuropsychopharmacology 2022;60:76-88. [DOI: 10.1016/j.euroneuro.2022.04.008] [Reference Citation Analysis]
14 Sun X, Xu H, Liu G, Chen J, Xu J, Li M, Liu L. A Robust Immuno-Prognostic Model of Non-Muscle-Invasive Bladder Cancer Indicates Dynamic Interaction in Tumor Immune Microenvironment Contributes to Cancer Progression. Front Genet 2022;13:833989. [DOI: 10.3389/fgene.2022.833989] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 Peng L, Yang J, Wang M, Zhou L. Editorial: Machine Learning-Based Methods for RNA Data Analysis. Front Genet 2022;13:828575. [DOI: 10.3389/fgene.2022.828575] [Reference Citation Analysis]
16 Waring AL, Hill J, Allen BM, Bretz NM, Le N, Kr P, Fuss D, Mortimer NT. Meta-Analysis of Immune Induced Gene Expression Changes in Diverse Drosophila melanogaster Innate Immune Responses. Insects 2022;13:490. [PMID: 35621824 DOI: 10.3390/insects13050490] [Reference Citation Analysis]
17 Antonatos C, Panoutsopoulou M, Georgakilas GK, Evangelou E, Vasilopoulos Y. Gene Expression Meta-Analysis of Potential Shared and Unique Pathways between Autoimmune Diseases under Anti-TNFα Therapy. Genes 2022;13:776. [DOI: 10.3390/genes13050776] [Reference Citation Analysis]
18 Bhattacharyya N, Gupta S, Sharma S, Soni A, Bagabir SA, Bhattacharyya M, Mukherjee A, Almalki AH, Alkhanani MF, Haque S, Ray AK, Malik MZ. CDK1 and HSP90AA1 Appear as the Novel Regulatory Genes in Non-Small Cell Lung Cancer: A Bioinformatics Approach. JPM 2022;12:393. [DOI: 10.3390/jpm12030393] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
19 Cervantes-gracia K, Chahwan R, Husi H. Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach. Front Genet 2022;13:828786. [DOI: 10.3389/fgene.2022.828786] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
20 Makinde FL, Tchamga MSS, Jafali J, Fatumo S, Chimusa ER, Mulder N, Mazandu GK. Reviewing and assessing existing meta-analysis models and tools. Brief Bioinform 2021;22:bbab324. [PMID: 34415019 DOI: 10.1093/bib/bbab324] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
21 Bhattacharyya N, Gupta S, Sharma S, Soni A, Bhattacharyya M, Mukherjee A, Ray AK, Malik MZ. CDK1 and HSP90AA1 appears as novel regulatory gene in Non-Small Cell Lung Cancer: A Bioinformatics Approach.. [DOI: 10.1101/2021.09.26.461854] [Reference Citation Analysis]
22 Waring AL, Hill J, Allen BM, Bretz NM, Le N, Kr P, Fuss D, Mortimer NT. Meta-analysis of immune induced gene expression changes in diverse Drosophila melanogaster innate immune responses.. [DOI: 10.1101/2021.09.23.461556] [Reference Citation Analysis]
23 Nwosu IO, Piccolo SR. A systematic review of datasets that can help elucidate relationships among gene expression, race, and immunohistochemistry-defined subtypes in breast cancer. Cancer Biol Ther 2021;22:417-29. [PMID: 34412551 DOI: 10.1080/15384047.2021.1953902] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
24 Mota APZ, Brasileiro ACM, Vidigal B, Oliveira TN, da Cunha Quintana Martins A, Saraiva MAP, de Araújo ACG, Togawa RC, Grossi-de-Sá MF, Guimaraes PM. Defining the combined stress response in wild Arachis. Sci Rep 2021;11:11097. [PMID: 34045561 DOI: 10.1038/s41598-021-90607-7] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
25 O'Brien MJ, Beijerink NJ, Wade CM. Genetics of canine myxomatous mitral valve disease. Anim Genet 2021;52:409-21. [PMID: 34028063 DOI: 10.1111/age.13082] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
26 Jendoubi T. Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer. Metabolites 2021;11:184. [PMID: 33801081 DOI: 10.3390/metabo11030184] [Cited by in Crossref: 16] [Cited by in F6Publishing: 17] [Article Influence: 8.0] [Reference Citation Analysis]
27 Malik A, Kim CB. Role of Transportome in the Gills of Chinese Mitten Crabs in Response to Salinity Change: A Meta-Analysis of RNA-Seq Datasets. Biology (Basel) 2021;10:39. [PMID: 33430106 DOI: 10.3390/biology10010039] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
28 Trevizan B, Recamonde-mendoza M. Ensemble Feature Selection Compares to Meta-analysis for Breast Cancer Biomarker Identification from Microarray Data. Computational Science and Its Applications – ICCSA 2021 2021. [DOI: 10.1007/978-3-030-86653-2_12] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
29 Li W, Ding Y, Yang Y, Sherratt RS, Park JH, Wang J. Parameterized algorithms of fundamental NP-hard problems: a survey. Hum Cent Comput Inf Sci 2020;10. [DOI: 10.1186/s13673-020-00226-w] [Cited by in Crossref: 18] [Cited by in F6Publishing: 19] [Article Influence: 6.0] [Reference Citation Analysis]