Tackling the widespread and critical impact of batch effects in high-throughput data

Exploration, normalization, and summaries of high density oligonucleotide array probe level data

Irizarry, R. A. (2003) · Biostatistics · doi:10.1093/biostatistics/4.2.249 · Cited by 10,603

Adjusting batch effects in microarray expression data using empirical Bayes methods

Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658

A comparison of normalization methods for high density oligonucleotide array data based on variance and bias

Bolstad, B. M., Irizarry, R. A., Åstrand, M., & Speed, T. P. (2003) · Bioinformatics · doi:10.1093/bioinformatics/19.2.185 · Cited by 8,328

Comprehensive genomic characterization defines human glioblastoma genes and core pathways

The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735

The International HapMap Project

†The International HapMap Consortium, Genotyping centres: Baylor College of Medicine and ParAllele BioScience, Gibbs, R. A., Belmont, J. W., Hardenbol, P., Willis, T. D., Yu, F., Chinese HapMap Consortium, Yang, H., Ch'ang, L.-Y., et al. (2003) · Nature · doi:10.1038/nature02168 · Cited by 6,057

Use of proteomic patterns in serum to identify ovarian cancer

Petricoin, E. F., Ardekani, A. M., Hitt, B. A., Levine, P. J., Fusaro, V. A., Steinberg, S. M., Mills, G. B., Simone, C., Fishman, D. A., Kohn, E. C., et al. (2002) · The Lancet · doi:10.1016/s0140-6736(02)07746-2 · Cited by 3,236

Principles of Numerical Taxonomy

Eysenck, H. J., & Eysenck, S. B. G. (2013) · Routledge eBooks · doi:10.4324/9780203766804-11 · Cited by 3,001

Singular value decomposition for genome-wide expression data processing and modeling

Alter, O., Brown, P. O., & Botstein, D. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.18.10101 · Cited by 1,989

Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis

Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930

Microarray data analysis: from disarray to consolidation and consensus

Allison, D. B., Cui, X., Page, G. P., & Sabripour, M. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1749 · Cited by 1,339

The age of the “ome”: Genome, transcriptome and proteome data set collection and analysis

Myers, A. (2011) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2011.11.015 · Cited by 16

Metabolic differences in ripening of Solanum lycopersicum ‘Ailsa Craig’ and three monogenic mutants

Beisken, S., Earll, M. E., Baxter, C., Portwood, D. E., Ament, Z., Kende, A., Hodgman, C. T., Seymour, G. B., Smith, R. A., Fraser, P. D., et al. (2014) · Scientific Data · doi:10.1038/sdata.2014.29 · Cited by 17

Batch Effect Removal via Batch-Free EncodingPreprint

Shaham, U. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/380816 · Cited by 17

Reference-informed evaluation of batch correction for single-cell omics data with overcorrection awareness

Hu, X., Li, H., Chen, M., Qian, J., & Jiang, H. (2025) · Communications Biology · doi:10.1038/s42003-025-07947-7 · Cited by 15

Proteome variance differences within populations of European whitefish (Coregonus lavaretus) originating from contrasting salinity environments

Papakostas, S., Vasemägi, A., Himberg, M., & Primmer, C. R. (2014) · Journal of Proteomics · doi:10.1016/j.jprot.2013.12.019 · Cited by 16

ChIPXpress: using publicly available gene expression data to improve ChIP-seq and ChIP-chip target gene ranking

Wu, G. Y., & Ji, H. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-188 · Cited by 16

A graph-based algorithm for RNA-seq data normalization

Tran, D.-T. T., Bhaskara, A., Might, M., & Kuberan, B. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227760 · Cited by 16

Sensitivity analysis based on the random forest machine learning algorithm identifies candidate genes for regulation of innate and adaptive immune response of chicken

Polewko-Klim, A., Lesiński, W., Golińska, A. K., Mnich, K., Siwek, M. Z., & Rudnicki, W. R. (2020) · Poultry Science · doi:10.1016/j.psj.2020.08.059 · Cited by 16

Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer

Zhang, L., Li, L., Zhan, Y., Zhu, Z., & Zhang, X. (2021) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.607090 · Cited by 15

Transfer transcriptomic signatures for infectious diseases

di Iulio, J., Bartha, I., Spreafico, R., Virgin, H. W., & Telenti, A. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2022486118 · Cited by 16

SurvCNN: A Discrete Time-to-Event Cancer Survival Estimation Framework Using Image Representations of Omics Data

Kalakoti, Y., Yadav, S., & Sundar, D. (2021) · Cancers · doi:10.3390/cancers13133106 · Cited by 17

Development of immune gene pair-based signature predictive of prognosis and immunotherapy in esophageal cancer

Cao, K., Ma, T., Ling, X., Liu, M., Jiang, X., Ma, K., Zhu, J., & Ma, J. (2021) · Annals of Translational Medicine · doi:10.21037/atm-21-5217 · Cited by 16

Subgroup-specific gene expression profiles and mixed epistasis in chronic lymphocytic leukemia

Luetge, A., Lu, J., Hüllein, J., Walther, T., Sellner, L., Wu, B., Rosenquist, R., Oakes, C. C., Dietrich, S., Huber, W. K., et al. (2023) · Haematologica · doi:10.3324/haematol.2022.281869 · Cited by 16

The parasite intraerythrocytic cycle and human circadian cycle are coupled during malaria infection

Motta, F. C., McGoff, K. A., Moseley, R. C., Cho, C.-Y., Kelliher, C., Smith, L. M., Ortiz, M. S., Leman, A. R., Campione, S. A., Devos, N., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2216522120 · Cited by 15

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data

Hutchings, C., Dawson, C. S., Krueger, T., Lilley, K. S., & Breckels, L. M. (2023) · F1000Research · doi:10.12688/f1000research.139116.1 · Cited by 18

Disentangling the age-related manner in the associations between gut microbiome and women’s health: a multi-cohort microbiome study

Dong, C., Guan, Q., Xu, W., Zhang, X., Jin, B., Yu, S., Xu, X., & Xia, Y. (2023) · Gut Microbes · doi:10.1080/19490976.2023.2290320 · Cited by 17

A survey on automated cell tracking: challenges and solutions

Yazdi, R., & Khotanlou, H. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18697-9 · Cited by 16

Meta-analysis of single-cell RNA sequencing co-expression in human neural organoids reveals their high variability in recapitulating primary tissue

Werner, J. M., & Gillis, J. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002912 · Cited by 21

Network-enabled gene expression analysis

Edwards, D., Wang, L., & Sørensen, P. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-167 · Cited by 16

Empirical evaluation of data normalization methods for molecular classification

Huang, H.-C. R., & Qin, L. (2018) · PeerJ · doi:10.7717/peerj.4584 · Cited by 16

Mediation analysis reveals common mechanisms of RUNX1 point mutations and RUNX1/RUNX1T1 fusions influencing survival of patients with acute myeloid leukemia

Hornung, R., Jurinović, V., Batcha, A. M. N., Bamopoulos, S. A., Rothenberg‐Thurley, M., Amler, S., Sauerland, M. C., Berdel, W. E., Wormann, B. J., Bohlander, S. K., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-29593-2 · Cited by 17

The impact of different sources of heterogeneity on loss of accuracy from genomic prediction models

Zhang, Y., Bernau, C., Parmigiani, G., & Waldron, L. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy044 · Cited by 16

Gene expression response to a nematode parasite in novel and native eel hosts

Bracamonte, S. E., Johnston, P. R., Monaghan, M. T., & Knopf, K. (2019) · Ecology and Evolution · doi:10.1002/ece3.5728 · Cited by 16

Biased Influences of Low Tumor Purity on Mutation Detection in Cancer

Cheng, J., He, J., Wang, S., Zhao, Z., Yan, H., Guan, Q., Li, J., Guo, Z., & Ao, L. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.533196 · Cited by 16

Identification of Immune-Related LncRNA Pairs for Predicting Prognosis and Immunotherapeutic Response in Head and Neck Squamous Cell Carcinoma

Wang, X., Cao, K., Guo, E., Mao, X., Guo, L., Zhang, C., Guo, J., Wang, G., Yang, X., Sun, J., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.658631 · Cited by 16

Preparation of single-cell suspensions of mouse glomeruli for high-throughput analysis

Korin, B., Chung, J.-J., Avraham, S., & Shaw, A. S. (2021) · Nature Protocols · doi:10.1038/s41596-021-00578-2 · Cited by 17

Construction of an immune-related lncRNA signature pair for predicting oncologic outcomes and the sensitivity of immunosuppressor in treatment of lung adenocarcinoma

Zhuang, J., Chen, Z., Chen, Z., Chen, J., Liu, M., Xu, X., Liu, Y., Yang, S., Hu, Z., & He, F. (2022) · Respiratory Research · doi:10.1186/s12931-022-02043-4 · Cited by 16

TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data

Lubbock, A. L. R., Katz, E., Harrison, D. J., & Overton, I. M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt529 · Cited by 15

Omic personality: implications of stable transcript and methylation profiles for personalized medicine

Tabassum, R., Sivadas, A., Agrawal, V., Tian, H., Arafat, D., & Gibson, G. C. (2015) · Genome Medicine · doi:10.1186/s13073-015-0209-4 · Cited by 16

Co-expression networks reveal the tissue-specific regulation of transcription and splicingPreprint

Saha, A., Kim, Y., Gewirtz, A. D., Jo, B., Gao, C., McDowell, I. C., Engelhardt, B. E., & Battle, A. J. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/078741 · Cited by 17

Correcting for batch effects in case-control microbiome studiesPreprint

Gibbons, S. M., Duvallet, C., & Alm, E. J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/165910 · Cited by 16

recount3: summaries and queries for large-scale RNA-seq expression and splicingPreprint

Wilks, C., Zheng, S., Chen, F. Y., Charles, R., Solomon, B., Ling, J. P., Imada, E. L., Zhang, D., Joseph, L., Leek, J. T., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.21.445138 · Cited by 16

Liver cancer multiomics reveals diverse protein kinase A disruptions convergently produce fibrolamellar hepatocellular carcinoma

Requena, D., Medico, J. A., Soto, L. F., Shirani, M., Saltsman, J. A., Torbenson, M. S., Coffino, P., & Simon, S. M. (2024) · Nature Communications · doi:10.1038/s41467-024-55238-2 · Cited by 17

A multi-omics pipeline integrating machine learning and spatial-cellular analysis identifies SASH1 as a prognostic biomarker and therapeutic target in head and neck squamous cell carcinoma

Dai, Z., Shan, X., Kang, Y., Chen, Y., Feng, Q., Cai, Z., & Xie, S. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003647 · Cited by 17

Designing RNA sequencing experiments: A practical guide to reproducible gene expression analysis

Antoszewski, K., Chmielewska, K., Jagiełło, K., & Puzyn, T. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.12.015 · Cited by 18

A Guide to Bayesian Optimization in Bioprocess Engineering

Siska, M., Pajak, E., Rosenthal, K., del Rio‐Chanona, E. A., von Lieres, E., & Helleckes, L. M. (2026) · Biotechnology and Bioengineering · doi:10.1002/bit.70129 · Cited by 15

Assessing ex situ genetic and ecogeographic conservation in a threatened but widespread oak after range‐wide collecting effort

Zumwalde, B. A., Fredlock, B., Bruns, E. B., Duckett, D. J., McCauley, R. A., Spence, E. S., & Hoban, S. (2022) · Evolutionary Applications · doi:10.1111/eva.13391 · Cited by 15

Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference

Heitz, M., Ma, Y., Kubal, S., & Schiebinger, G. R. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-040324-030052 · Cited by 20

Bayesian hierarchical model of protein-binding microarrayk-mer data reduces noise and identifies transcription factor subclasses and preferredk-mers

Jiang, B., Liu, J. S., & Bulyk, M. L. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt152 · Cited by 17

ChIP-PED enhances the analysis of ChIP-seq and ChIP-chip data

Wu, G. Y., Yustein, J. T., McCall, M. N., Zilliox, M. J., Irizarry, R. A., Zeller, K. I., Dang, C. V., & Ji, H. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt108 · Cited by 15

ARTS: automated randomization of multiple traits for study design

Maienschein‐Cline, M., Lei, Z., Gardeux, V., Abbasi, T. A., Machado, R. F., Gordeuk, V. R., Desai, A. A., Saraf, S. L., Bahroos, N., & Lussier, Y. A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu075 · Cited by 15

Neonatal levels of adiponectin, interleukin-10 and interleukin-12 are associated with the risk of developing type 1 diabetes in childhood and adolescence: A nationwide Danish case-control study

Thorsen, S. U., Pipper, C. B., Eising, S., Skogstrand, K., HOUGAARD, D. M., Svensson, J., & Pociot, F. (2016) · Clinical Immunology · doi:10.1016/j.clim.2016.11.007 · Cited by 15

Growth medium and incubation temperature alter the Pseudogymnoascus destructans transcriptome: implications in identifying virulence factors

Donaldson, M. E., Davy, C. M., Vanderwolf, K. J., Willis, C. K. R., Saville, B. J., & Kyle, C. J. (2018) · Mycologia · doi:10.1080/00275514.2018.1438223 · Cited by 15

Influence of batch effect correction methods on drug induced differential gene expression profiles

Zhou, W. Z., Koudijs, K. K. M., & Böhringer, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3028-6 · Cited by 15

N-glycomic Profile in Combat Related Post-Traumatic Stress Disorder

Tudor, L., Erjavec, G. N., Perković, M. N., Konjevod, M., Štrac, D. Š., Uzun, S., Kozumplik, O., Jovanović, T., Lauc, G., & Pivac, N. (2019) · Biomolecules · doi:10.3390/biom9120834 · Cited by 15

Screening as a strategy to drive regenerative medicine research

Vermeulen, S., & de Boer, J. (2020) · Methods · doi:10.1016/j.ymeth.2020.04.004 · Cited by 14

Performance evaluation of transcriptomics data normalization for survival risk prediction

Ni, A., & Qin, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab257 · Cited by 15

Tissue- and ethnicity-independent hypervariable DNA methylation states show evidence of establishment in the early human embryo

Derakhshan, M., Kessler, N. J., Ishida, M., Demetriou, C., Brucato, N., Moore, G. E., Fall, C. H. D., Chandak, G. R., Ricaut, F., Prentice, A. M., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac503 · Cited by 15

Immune-Signatures for Lung Cancer Diagnostics: Evaluation of Protein Microarray Data Normalization Strategies

Brezina, S., Soldo, R., Kreuzhuber, R., Hofer, P., Gsur, A., & Weinhaeusel, A. (2015) · Microarrays · doi:10.3390/microarrays4020162 · Cited by 15

Comparison of different normalization assumptions for analyses of DNA methylation data from the cancer genome

Wang, D., Zhang, Y., Huang, Y., Li, P., Wang, M., Wu, R., Cheng, L., Zhang, W., Zhang, Y., Li, B., et al. (2012) · Gene · doi:10.1016/j.gene.2012.06.075 · Cited by 15

Assessment of gene expression in peripheral blood using RNAseq before and after weight restoration in anorexia nervosa

Kim, Y., Trace, S. E., Crowley, J. J., Brownley, K. A., Hamer, R. M., Pisetsky, D. S., Sullivan, P. F., & Bulik, C. M. (2013) · Psychiatry Research · doi:10.1016/j.psychres.2013.05.026 · Cited by 18

Dysregulation of TFDP1 and of the cell cycle pathway in high-grade glioblastoma multiforme: a bioinformatic analysis

Lu, X., Lv, X. D., Ren, Y. H., Yang, W. D., Li, Z. B., Zhang, L., & Bai, X. (2016) · Genetics and Molecular Research · doi:10.4238/gmr.15027646 · Cited by 15

Comprehensive Analysis of MILE Gene Expression Data Set Advances Discovery of Leukaemia Type and Subtype Biomarkers

Łabaj, W., Papież, A., Polański, A., & Polańska, J. (2017) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-017-0216-9 · Cited by 15

Pan-cancer analysis of systematic batch effects on somatic sequence variations

Choi, J.-H., Hong, S.-E., & Woo, H. G. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1627-7 · Cited by 14

Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta‐Omics Data in Personalized and Precision Medicine

Oh, S., & Li, R. W. (2024) · Advanced Science · doi:10.1002/advs.202400458 · Cited by 15

A data harmonization pipeline to leverage external controls and boost power in GWAS

Chen, D., Tashman, K. C., Palmer, D. S., Neale, B. M., Roeder, K., Bloemendal, A., Churchhouse, C. L., & Ke, Z. T. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab261 · Cited by 16

Surfing the Big Data Wave: Omics Data Challenges in Transplantation

Ba, R., Geffard, E., Douillard, V., Simon, F., Mesnard, L., Vince, N., Gourraud, P., & Limou, S. (2021) · Transplantation · doi:10.1097/tp.0000000000003992 · Cited by 14

Individualized pathway activity algorithm identifies oncogenic pathways in pan-cancer analysis

Ke, X., Wu, H., Chen, Y.-X., Guo, Y. P., Yao, S., Guo, M., Duan, Y., Wang, N., Shi, W., Wang, C., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104014 · Cited by 15

Detection and reduction of systematic bias in high-throughput rupture experiments

Wu, H., Zhang, X., Zhou, Y., Blanchet, J., Suo, Z., & Lu, T. (2023) · Journal of the Mechanics and Physics of Solids · doi:10.1016/j.jmps.2023.105249 · Cited by 14

Predicting the flavor potential of green coffee beans with machine learning-assisted visible/near-infrared hyperspectral imaging (Vis-NIR HSI): Batch effect removal and few-shot learning framework

Wu, M., Shi, Z., Zhang, H., Wang, R., Chu, J., Liu, S., Zhang, H., Bi, H., Huang, W., Zhou, R. H., et al. (2025) · Food Control · doi:10.1016/j.foodcont.2025.111310 · Cited by 14

A reproducible approach to high-throughput biological data acquisition and integration

Börnigen, D., Moon, Y. S., Rahnavard, A., Waldron, L., McIver, L. J., Shafquat, A., Franzosa, E. A., Miropolsky, L., Sweeney, C. J., Morgan, X. C., et al. (2015) · PeerJ · doi:10.7717/peerj.791 · Cited by 15

Mapping eQTL Networks with Mixed Graphical Markov Models

Tur, I., Roverato, A., & Castelo, R. (2014) · Genetics · doi:10.1534/genetics.114.169573 · Cited by 14

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Li, G., Bankhead, P., Dunne, P. D., O’Reilly, P. G., James, J. A., Salto‐Tellez, M., Hamilton, P. W., & McArt, D. G. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw044 · Cited by 15

Analysis of ChIP-seq Data in R/Bioconductor

de Santiago, I., & Carroll, T. D. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7380-4_17 · Cited by 14

microbiomeDASim: Simulating longitudinal differential abundance for microbiome data

Williams, J., Bravo, H. C., Tom, J. A., & Paulson, J. N. (2020) · F1000Research · doi:10.12688/f1000research.20660.2 · Cited by 14

BatchDTA: implicit batch alignment enhances deep learning-based drug–target affinity estimation

Luo, H., Xiang, Y., Fang, X., Lin, W. R., Wang, F., Wu, H. R., & Wang, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac260 · Cited by 16

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning

Yang, S., Shen, T., Fang, Y., Wang, X., Zhang, J., Yang, W., Huang, J., & Han, X. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.12.007 · Cited by 15

A statistical approach to selecting and confirming validation targets in -omics experiments

Leek, J. T., Taub, M. A., & Rasgon, J. L. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-150 · Cited by 14

Hands-on Workshops as An Effective Means of Learning Advanced Technologies Including Genomics, Proteomics and Bioinformatics

Reisdorph, N. A., Stearman, R. S., Kechris, K. J., Phang, T. L., Reisdorph, R. M., Prenni, J. E., Erle, D. J., Coldren, C. D., Schey, K. L., Nesvizhskii, A. I., et al. (2013) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2013.10.002 · Cited by 14

A tuberculosis ontology for host systems biology

Levine, D., Dutta, N. K., Eckels, J., Scanga, C. A., Stein, C. M., Mehra, S., Kaushal, D., Karakousis, P. C., & Salamon, H. (2015) · Tuberculosis · doi:10.1016/j.tube.2015.05.012 · Cited by 14

Application of Genomic and Proteomic Technologies in Biomarker Discovery

Fertig, E. J., Slebos, R. J. C., & Chung, C. H. (2012) · American Society of Clinical Oncology Educational Book · doi:10.14694/edbook_am.2012.32.156 · Cited by 14

Empirical Bayes shrinkage and false discovery rate estimation, allowing for unwanted variation

Gerard, D., & Stephens, M. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy029 · Cited by 20

The relative resistance of children to sepsis mortality: from pathways to drug candidates

Joachim, R. B., Altschuler, G. M., Hutchinson, J. N., Wong, H. R., Hide, W. A., & Kobzik, L. (2018) · Molecular Systems Biology · doi:10.15252/msb.20177998 · Cited by 14

Weighted elastic net for unsupervised domain adaptation with application to age prediction from DNA methylation data

Handl, L., Jalali, A., Scherer, M., Eggeling, R., & Pfeifer, N. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz338 · Cited by 13

How many cells are enough for single-cell infrared spectroscopy?

Wang, Y., Dai, W., Wang, Y., Liu, J., Liu, Z., Li, Y., Li, X., Hu, J., & Lü, J. (2020) · Chemical Communications · doi:10.1039/d0cc00067a · Cited by 14

Twin Study of Early-Onset Major Depression Finds DNA Methylation Enrichment for Neurodevelopmental GenesPreprint

Roberson‐Nay, R., Wolen, A. R., Lapato, D. M., Lancaster, E. E., Webb, B. T., Verhulst, B., Hettema, J. M., & York, T. P. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/422345 · Cited by 14

Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation

Prabhudesai, S., Wang, N. C., Ahluwalia, V., Huan, X., Bapuraj, J. R., Banovic, N., & Rao, A. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.740353 · Cited by 13

The Emerging Role of Omics-Based Approaches in Plant Virology

Samarskaya, V. O., Spechenkova, N. A., Kalinina, N. O., Love, A. J., & Taliansky, M. E. (2025) · Viruses · doi:10.3390/v17070986 · Cited by 14

How thoughtful experimental design can empower biologists in the omics era

Wagner, M. R., & Kleiner, M. (2025) · Nature Communications · doi:10.1038/s41467-025-62616-x · Cited by 15

Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning

Lalman, C., Yang, Y., & Walker, J. L. (2025) · Cells · doi:10.3390/cells14171315 · Cited by 16

FilTar: using RNA-Seq data to improve microRNA target prediction accuracy in animals

Bradley, T., & Moxon, S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa007 · Cited by 13

Identifying ceRNA Networks Associated With the Susceptibility and Persistence of Atrial Fibrillation Through Weighted Gene Co-Expression Network Analysis

Liu, Y., Liu, N., Bai, F., & Liu, Q. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.653474 · Cited by 13

Weaker selection on genes with treatment-specific expression consistent with a limit on plasticity evolution in Arabidopsis thaliana

Roberts, M. D., & Josephs, E. B. (2023) · Genetics · doi:10.1093/genetics/iyad074 · Cited by 13

Global geometry of chemical graph neural network representations in terms of chemical moieties

El-Samman, A. M., Incé A. Husain, Huynh, M., Castro, S. D., Morton, B., Baerdemacker, S. D., & Baerdemacker, S. D. (2024) · Digital Discovery · doi:10.1039/d3dd00200d · Cited by 14

Integrated Proteomics Characterization of NLRP3 Inflammasome Inhibitor MCC950 in Monocytic Cell Line Confirms Direct MCC950 Engagement with Endogenous NLRP3

Zhao, H., Kumar, P., Sobreira, T. J. P., Smith, M. L., Novick, S. J., Johansson, A., Łuchniak, A., Zhang, A. X., Woollard, K. J., Larsson, N., et al. (2024) · ACS Chemical Biology · doi:10.1021/acschembio.3c00777 · Cited by 13

A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies

Van, R., Alvarez, D., Mize, T. J., Gannavarapu, S., Reddy, L. C., Nasoz, F., & Han, M. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05801-x · Cited by 20

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Lo, M. C. K., Siu, D. M. D., Lee, K. C. M., Wong, J. S. J., Yeung, M. C. F., Hsin, M. K. Y., Ho, J., & Tsia, K. K. (2024) · Advanced Science · doi:10.1002/advs.202307591 · Cited by 13

Histopathologic Analysis of Human Kidney Spatial Transcriptomics Data

Isnard, P., Li, D., Xuanyuan, Q., Wu, H., & Humphreys, B. D. (2024) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.06.011 · Cited by 13

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Guo, B., Ling, W., Kwon, S. H., Panwar, P., Ghazanfar, S., Martinowich, K., & Hicks, S. C. (2025) · Small Methods · doi:10.1002/smtd.202401194 · Cited by 15

A phylogenomic approach to disentangling the evolution of the large and diverse daisy tribe Anthemideae (Asteraceae)

Ruiz, D. C., Vallès, J., Bayer, R. J., Palazzesi, L., Pellicer, J., Pérez-Lorenzo, I., Maurin, O., Françoso, E., Roy, S. R., Leitch, I. J., et al. (2024) · Journal of Systematics and Evolution · doi:10.1111/jse.13118 · Cited by 13

Improved Variant Calling Accuracy by Merging Replicates in Whole-Exome Sequencing Studies

Zhang, Y., Li, B., Li, C., Cai, Q., Zheng, W., & Long, J. (2014) · BioMed Research International · doi:10.1155/2014/319534 · Cited by 13

Confounding Factors in the Transcriptome Analysis of an In-Vivo Exposure Experiment

Bruning, O., Rodenburg, W., Wackers, P. F. K., van Oostrom, C., Jonker, M. J., Dekker, R. J., Rauwerda, H., Ensink, W. A., de Vries, A., & Breit, T. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0145252 · Cited by 13

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Li, M., Li, H., Hong, G., Tang, Z., Liu, G., Lin, X., Lin, M., Qi, L., & Guo, Z. (2019) · BMC Cancer · doi:10.1186/s12885-019-5274-4 · Cited by 13

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Kim, J., Kusko, R. L., Zeskind, B. J., Zhang, J., & Escalante-Chong, R. (2021) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2021.188548 · Cited by 13

Towards Personalized Treatment Strategies for Esophageal Adenocarcinoma; A Review on the Molecular Characterization of Esophageal Adenocarcinoma and Current Research Efforts on Individualized Curative Treatment Regimens

Hoefnagel, S. J. M., Boonstra, J. J., Russchen, M. J. A. M., & Krishnadath, K. K. (2021) · Cancers · doi:10.3390/cancers13194881 · Cited by 13

IMGG: Integrating Multiple Single-Cell Datasets through Connected Graphs and Generative Adversarial Networks

Wang, X., Zhang, C., Zhang, Y., Meng, X., Zhang, Z., Shi, X., & Song, T. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23042082 · Cited by 14

Protocol for analysis of RNA-sequencing and proteome profiling data for subgroup identification and comparison

Yang, K., & Gorski, S. M. (2022) · STAR Protocols · doi:10.1016/j.xpro.2022.101283 · Cited by 13

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Donyavi, M. H., Salehi-Mazandarani, S., & Nikpour, P. (2022) · PubMed Central · doi:10.22038/ijbms.2022.61570.13623 · Cited by 11

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Xun, D., Wang, R., Zhang, X., & Wáng, Y. (2024) · The Innovation · doi:10.1016/j.xinn.2023.100541 · Cited by 14

Rank-based predictors for response and prognosis of neoadjuvant taxane-anthracycline-based chemotherapy in breast cancer

Zhang, L., Hao, C., Shen, X., Hong, G., Li, H., Zhou, X., Liu, C., & Guo, Z. (2013) · Breast Cancer Research and Treatment · doi:10.1007/s10549-013-2566-2 · Cited by 13

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 254

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Goh, W. W. B., Wang, W., & Wong, L. (2017) · Trends in biotechnology · doi:10.1016/j.tibtech.2017.02.012 · Cited by 419

BatchI: batch effect Identification in high-throughput screening data using a dynamic programming algorithm

Papiez, A., Marczyk, M., Polanska, J., & Polanski, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty900 · Cited by 26

Addressing batch effects

González, J. R., & Cáceres, A. (2019) · Chapman & Hall/CRC eBooks · doi:10.1201/9780429440557-6

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Oytam, Y., Sobhanmanesh, F., Duesing, K., Bowden, J. C., Osmond-McLeod, M. J., & Ross, J. P. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1212-5 · Cited by 94