reComBat: batch-effect removal in large-scale multi-source gene-expression data integration

Highly accurate protein structure prediction with AlphaFold

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Fast, sensitive and accurate integration of single-cell data with Harmony

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NCBI GEO: archive for functional genomics data sets—update

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Complete genome sequence of Pseudomonas aeruginosa PAO1, an opportunistic pathogen

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ComBat-seq: batch effect adjustment for RNA-seq count data

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A benchmark of batch-effect correction methods for single-cell RNA sequencing data

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scGen predicts single-cell perturbation responses

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Frozen robust multiarray analysis (fRMA)

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Batch effect removal methods for microarray gene expression data integration: a survey

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Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks

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Antibiotic resistance: turning evolutionary principles into clinical reality

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Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

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Merging two gene-expression studies via cross-platform normalization

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A novel type II secretion system in Pseudomonas aeruginosa

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Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

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The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

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Diagnostics and correction of batch effects in large‐scale proteomic studies: a tutorial

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Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data

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FptA, the Fe(III)-pyochelin receptor of Pseudomonas aeruginosa: a phenolate siderophore receptor homologous to hydroxamate siderophore receptors

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Essential PchG-Dependent Reduction in Pyochelin Biosynthesis of Pseudomonas aeruginosa

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Chemotaxis proteins and transducers for aerotaxis inPseudomonas aeruginosa

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Detect and correct bias in multi-site neuroimaging datasets

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Phosphate Starvation Promotes Swarming Motility and Cytotoxicity of Pseudomonas aeruginosa

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Nucleotide sequence of pvdD, a pyoverdine biosynthetic gene from Pseudomonas aeruginosa: PvdD has similarity to peptide synthetases

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Assembly of Fimbrial Structures inPseudomonas aeruginosa: Functionality and Specificity of Chaperone-Usher Machineries

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Generative adversarial networks simulate gene expression and predict perturbations in single cellsPreprint

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Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench

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Simulating ComBat: how batch correction can lead to the systematic introduction of false positive results in DNA methylation microarray studies

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A secondtonBgene inPseudomonas aeruginosais linked to theexbBandexbDgenes

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Structural Characterization of the Hemophore HasAp from Pseudomonas aeruginosa: NMR Spectroscopy Reveals Protein−Protein Interactions between Holo-HasAp and Hemoglobin,

Alontaga, A. Y., Rodrı́guez, J. C., Schönbrunn, E., Becker, A., Funke, T., Yukl, E. T., Hayashi, T., Stobaugh, J. T., Moënne‐Loccoz, P., & Rivera, M. (2008) · Biochemistry · doi:10.1021/bi801860g · Cited by 75 · see citing works

Functional Characterization of an Aminotransferase Required for Pyoverdine Siderophore Biosynthesis in Pseudomonas aeruginosa PAO1

Vandenende, C., Vlasschaert, M., & Seah, S. Y. K. (2004) · Journal of Bacteriology · doi:10.1128/jb.186.17.5596-5602.2004 · Cited by 72 · see citing works

NormAE: Deep Adversarial Learning Model to Remove Batch Effects in Liquid Chromatography Mass Spectrometry-Based Metabolomics Data

Rong, Z., Tan, Q., Cao, L., Zhang, L., Deng, K., Huang, Y., Zhu, Z., Li, Z., & Li, K. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.9b05460 · Cited by 72 · see citing works

Adversarial deconfounding autoencoder for learning robust gene expression embeddings

Dincer, A. B., Janizek, J. D., & Lee, S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa796 · Cited by 71 · see citing works

The 1.8 Å Crystal Structure of PA2412, an MbtH-like Protein from the Pyoverdine Cluster of Pseudomonas aeruginosa

Drake, E. J., Cao, J., Qu, J., Shah, M. B., Straubinger, R. M., & Gulick, A. M. (2007) · Journal of Biological Chemistry · doi:10.1074/jbc.m611833200 · Cited by 66 · see citing works

The PvdRT‐OpmQ efflux pump controls the metal selectivity of the iron uptake pathway mediated by the siderophore pyoverdine in Pseudomonas aeruginosa

Hannauer, M., Braud, A., Hoegy, F., Ronot, P., Boos, A., & Schalk, I. J. (2011) · Environmental Microbiology · doi:10.1111/j.1462-2920.2011.02674.x · Cited by 62 · see citing works

Fumarase C activity is elevated in response to iron deprivation and in mucoid, alginate-producing Pseudomonas aeruginosa: cloning and characterization of fumC and purification of native fumC

Hassett, D. J., Howell, M. L., Sokol, P. A., Vasil, M. L., & Dean, G. E. (1997) · Journal of Bacteriology · doi:10.1128/jb.179.5.1442-1451.1997 · Cited by 59 · see citing works

Towards a data-integrated cell

Malod‐Dognin, N., Petschnigg, J., Windels, S. F. L., Povh, J., Hemingway, H., Ketteler, R., & Pržulj, N. (2019) · Nature Communications · doi:10.1038/s41467-019-08797-8 · Cited by 54 · see citing works

Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments

Yu, X., Aghababazadeh, F. A., Chen, Y. A., & Fridley, B. L. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0849-4_9 · Cited by 54 · see citing works

The olsA gene mediates the synthesis of an ornithine lipid in Pseudomonas aeruginosa during growth under phosphate-limiting conditions, but is not involved in antimicrobial peptide susceptibility

Lewenza, S., Falsafi, R., Bains, M., Rohs, P. D. A., Stupak, J., Sprott, G. D., & Hancock, R. E. W. (2011) · FEMS Microbiology Letters · doi:10.1111/j.1574-6968.2011.02295.x · Cited by 46 · see citing works

Phosphorus stress induces the synthesis of novel glycolipids in Pseudomonas aeruginosa that confer protection against a last-resort antibiotic

Jones, R. A., Shropshire, H., Zhao, C., Murphy, A. R. J., Lidbury, I. D. E. A., Wei, T., Scanlan, D. J., & Chen, Y. (2021) · The ISME Journal · doi:10.1038/s41396-021-01008-7 · Cited by 39 · see citing works

Antibiotic resistance: Time of synthesis in a post-genomic age

Gil‐Gil, T., Ochoa-Sánchez, L. E., Baquero, F., & Martínez, J. L. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.05.034 · Cited by 32 · see citing works

AntR-mediated bidirectional activation of antA and antR, anthranilate degradative genes in Pseudomonas aeruginosa

Kim, S.-K., Im, S.-J., Yeom, D.-H., & Lee, J. (2012) · Gene · doi:10.1016/j.gene.2012.05.004 · Cited by 25 · see citing works

Pseudomonas aeruginosa IscR-Regulated Ferredoxin NADP(+) Reductase Gene (fprB) Functions in Iron-Sulfur Cluster Biogenesis and Multiple Stress Response

Romsang, A., Duang-nkern, J., Wirathorn, W., Vattanaviboon, P., & Mongkolsuk, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0134374 · Cited by 24 · see citing works

A High-Throughput Method for Identifying Novel Genes That Influence Metabolic Pathways Reveals New Iron and Heme Regulation in Pseudomonas aeruginosa

Glanville, D. G., Mullineaux-Sanders, C., Corcoran, C. J., Burger, B. T., Imam, S., Donohue, T. J., & Ulijasz, A. T. (2021) · mSystems · doi:10.1128/msystems.00933-20 · Cited by 23 · see citing works

Characterization of a novel two-partner secretion system implicated in the virulence of Pseudomonas aeruginosa

Faure, L. M., Garvis, S., de Bentzmann, S., & Bigot, S. (2014) · Microbiology · doi:10.1099/mic.0.079616-0 · Cited by 23 · see citing works

The Activity of the Pseudomonas aeruginosa Virulence Regulator σVreI Is Modulated by the Anti-σ Factor VreR and the Transcription Factor PhoB

Quesada, J. M., Otero‐Asman, J. R., Bastiaansen, K. C., Civantos, C., & Llamas, M. A. (2016) · Frontiers in Microbiology · doi:10.3389/fmicb.2016.01159 · Cited by 21 · see citing works

An Attempt for Combining Microarray Data Sets by Adjusting Gene Expressions

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An efficient parallel algorithm of N-hop neighborhoods on graphs in distributed environment

Liu, W., & Li, Z. (2018) · Frontiers of Computer Science · doi:10.1007/s11704-018-7167-0 · Cited by 7 · see citing works

Assessing and mitigating batch effects in large-scale omics studies

Yu, Y., Mai, Y., Zheng, Y., & Shi, L. M. (2024) · Genome biology · doi:10.1186/s13059-024-03401-9 · Cited by 231 · see citing works

Harnessing Multi-Omics and Predictive Modeling for Climate-Resilient Crop Breeding: From Genomes to Fields

Amin, A., Zaman, W., & Park, S. (2025) · Genes · doi:10.3390/genes16070809 · Cited by 82 · see citing works

Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer

Borràs, D. M., Verbandt, S., Ausserhofer, M., Sturm, G., Lim, J., Verge, G. A., Vanmeerbeek, I., Laureano, R. S., Govaerts, J., Sprooten, J., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00605-4 · Cited by 67 · see citing works

Systematic discovery of CRISPR-boosted CAR T cell immunotherapies

Datlinger, P., Pankevich, E. V., Arnold, C. D., Pranckevicius, N., Lin, J., Romanovskaia, D., Schaefer, M., Piras, F., Orts, A.-C., Nemc, A., et al. (2025) · Nature · doi:10.1038/s41586-025-09507-9 · Cited by 61 · see citing works

The polyamine-regulating enzyme SSAT1 impairs tissue regulatory T cell function in chronic cutaneous inflammation

Neuwirth, T., Malzl, D., Knapp, K., Tsokkou, P., Kleißl, L., Gabriel, A., Reininger, B., Freystätter, C., Marella, N., Kutschat, A. P., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.02.011 · Cited by 31 · see citing works

Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets

Schumann, Y., Gocke, A., & Neumann, J. E. (2024) · PROTEOMICS · doi:10.1002/pmic.202400100 · Cited by 28 · see citing works

Thinking points for effective batch correction on biomedical data

Hui, H. W. H., Kong, W., & Goh, W. W. B. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae515 · Cited by 25 · see citing works

Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts

Kamat, P., Macaluso, N. C., Li, Y., Agrawal, A., Winston, A., Pan, L., Stewart, T., Starich, B., Milcik, N., Min, C., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads1875 · Cited by 24 · see citing works

A lipid atlas of the human kidney

Farrow, M. A., Tideman, L. E. M., Neumann, E. K., Patterson, N. H., Migas, L. G., Colley, M. E., Allen, J. L., Rivera, E. S., Romer, C. E., Yang, H., et al. (2025) · Science Advances · doi:10.1126/sciadv.adu3730 · Cited by 28 · see citing works

Operational Taxon-Function Framework in MetaX: Unveiling Taxonomic and Functional Associations in Metaproteomics

Wu, Q., Ning, Z., Zhang, A., Zhang, X., Sun, Z., & Figeys, D. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.4c06645 · Cited by 19 · see citing works

Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects

Kriukov, D. V., Kuzmina, E., Efimov, E., Dylov, D. V., & Khrameeva, E. E. (2024) · Aging Cell · doi:10.1111/acel.14283 · Cited by 19 · see citing works

ComBat models for harmonization of resting-state EEG features in multisite studies

Jaramillo-Jiménez, A., Tovar, D., Mantilla-Ramos, Y.-J., Ochoa-Gomez, J. F., Bonanni, L., & Brønnick, K. (2024) · Clinical Neurophysiology · doi:10.1016/j.clinph.2024.09.019 · Cited by 16 · see citing works

Enhanced metagenomic strategies for elucidating the complexities of gut microbiota: a review

Li, X., & Lu, H. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1626002 · Cited by 13 · see citing works

A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data

Pan, L., Gao, Q., Wei, K., Yu, Y., Qin, G., & Wang, T. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012739 · Cited by 11 · see citing works

Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

Han, M., Wang, Y., Huang, X., Li, P., Liang, X., Wang, R., & Bao, K. (2023) · European journal of medical research · doi:10.1186/s40001-023-01311-3 · Cited by 10 · see citing works

Dual adversarial deconfounding autoencoder for joint batch-effects removal from multi-center and multi-scanner radiomics data

Cavinato, L., Massi, M. C., Sollini, M., Kirienko, M., & Ieva, F. (2023) · Scientific Reports · doi:10.1038/s41598-023-45983-7 · Cited by 5 · see citing works

DNA methylation classifier to diagnose pancreatic ductal adenocarcinoma metastases from different anatomical sites

Calina, T. G., Pérez, E., Grafenhorst, E., Benhamida, J. K., Schallenberg, S., Popescu, A., Koch, I., Janik, T., Chen, B., Ihlow, J., et al. (2024) · Clinical Epigenetics · doi:10.1186/s13148-024-01768-x · Cited by 5 · see citing works

iComBat: An incremental framework for batch effect correction in DNA methylation array data

Tomo, Y., & Nakaki, R. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.09.014 · Cited by 4 · see citing works

Characterizing resting-state EEG oscillatory and aperiodic activity in neurodegenerative diseases: A multicentric study

Jaramillo-Jiménez, A., Mantilla-Ramos, Y.-J., Tovar, D., Lopera, F. J., Aguillon, D. F., Ochoa-Gómez, J. F., Paquet, C., Gaubert, S., Pardini, M., Arnaldi, D., et al. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111080 · Cited by 3 · see citing works

Modelling single-stress omics integration with HIVE enables the identification of responding signatures to multifactorial stress combinations in plantsPreprint

Calia, G., Marguerit, S., Mota, A. P. Z., Vidal, M., Schuler, H., Brasileiro, A. C. M., Guimarães, P. M., & Bottini, S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.04.583290 · Cited by 2 · see citing works

Age-related evolution of human gingiva towards a fibrotic-like connective phenotype

Cecchin-Albertoni, C., Deny, O., Pieruccioni, L., Ousset, M., Oreja-Fuentes, P., Arnaud, E., Guissard, C., Casteilla, L., Planat‐Benard, V., Kémoun, P., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-29089-w · Cited by 1 · see citing works

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Liu, S., Li, S. X., & Qian, K. (2026) · Advanced Intelligent Discovery · doi:10.1002/aidi.202500249 · Cited by 2 · see citing works

Batch correction for large-scale mass spectrometry imaging experiments

Sparre, A. A., & Jensen, O. N. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag360 · Cited by 1 · see citing works

Additional file 5 of Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

Han, M., Wang, Y., Huang, X., Li, P., Liang, X., Wang, R., & Bao, K. (2024) · Figshare · doi:10.6084/m9.figshare.26640976

Additional file 4 of Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

Han, M., Wang, Y., Huang, X., Li, P., Liang, X., Wang, R., & Bao, K. (2024) · Figshare · doi:10.6084/m9.figshare.26640973

Additional file 3 of Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

Han, M., Wang, Y., Huang, X., Li, P., Liang, X., Wang, R., & Bao, K. (2024) · Figshare · doi:10.6084/m9.figshare.26640970

Additional file 1 of Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

Han, M., Wang, Y., Huang, X., Li, P., Liang, X., Wang, R., & Bao, K. (2024) · Figshare · doi:10.6084/m9.figshare.26640964

Additional file 6 of Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

Han, M., Wang, Y., Huang, X., Li, P., Liang, X., Wang, R., & Bao, K. (2024) · Figshare · doi:10.6084/m9.figshare.26640979

Additional file 2 of Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

Han, M., Wang, Y., Huang, X., Li, P., Liang, X., Wang, R., & Bao, K. (2024) · Figshare · doi:10.6084/m9.figshare.26640967

reComBat-seq: Regularized negative binomial regression for batch-effect correction in underdetermined transcriptomics datasetsPreprint

Stoyanova, Z., Menche, J., & Malzl, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.27.728166

Impacts of batch effects on the performance of machine learning classifiers across multiple studiesPreprint

Raab, P., Johnson, W. E., & Piccolo, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.24.734352

Immunohistochemistry-compatible gel-assisted mass spectrometry imagingPreprint

Hibbard, M. C., Chan, Y. H., Dobbelmann, H. P., Pathmasiri, K. C., Cologna, S. M., & Gao, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.31.742113

A novel batch effect correction framework for robust integration of high-variance data via a global-information virtual reference batch

Liu, Y., Du, L., Jiang, J., Tong, X., Xu, J., Wang, J., & Lai, X. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2026.1877381

reComBat: Batch effect removal in large-scale, multi-source omics data integration

Adamer, M. F., Brüningk, S. C., Tejada-Arranz, A., Estermann, F., Basler, M., & Borgwardt, K. M. (2021) · edoc (University of Basel) · doi:10.5451/unibas-ep87154

reComBat-seq: removing batch effects of large datasets with regularized negative binomial regression

Stoyanova, Z. (2025) · University of Vienna · doi:10.25365/thesis.79729

reComBat-seq: Regularized negative binomial regression for batch-effect correction in underdetermined transcriptomics datasetsPreprint

Stoyanova, Z., Menche, J., & Malzl, D. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.27.728166

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