Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning

XGBoost: A Scalable Tree Boosting System

Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · see citing works

limma powers differential expression analyses for RNA-sequencing and microarray studies

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works

The <tt>sva</tt> package for removing batch effects and other unwanted variation in high-throughput experiments

Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts034 · Cited by 6,231 · see citing works

XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification

Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. A., & Siuzdak, G. E. (2006) · Analytical Chemistry · doi:10.1021/ac051437y · Cited by 5,296 · see citing works

MetaboAnalyst: a web server for metabolomic data analysis and interpretation

Xia, J., Psychogios, N., Young, N., & Wishart, D. S. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp356 · Cited by 2,238 · see citing works

Spheroid-based drug screen: Considerations and practical approach

Friedrich, J., Seidel, C., Ebner, R., & Kunz-Schughart, L. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.226 · Cited by 1,742 · see citing works

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

Zhang, Y., Parmigiani, G., & Johnson, W. E. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa078 · Cited by 1,566 · see citing works

Integrative analysis of multimodal mass spectrometry data in MZmine 3

Schmid, R., Heuckeroth, S., Korf, A., Smirnov, A., Myers, O., Dyrlund, T. S., Bushuiev, R., Murray, K. J., Hoffmann, N., Lu, M., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01690-2 · Cited by 1,241 · see citing works

metaX: a flexible and comprehensive software for processing metabolomics data

Wen, B., Mei, Z., Zeng, C., & Liu, S. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1579-y · Cited by 987 · see citing works

NOREVA: normalization and evaluation of MS-based metabolomics data

Li, B., Tang, J., Yang, Q., Li, S., Cui, X., Li, Y., Chen, Y., Xue, W., Li, X., & Zhu, F. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx449 · Cited by 357 · see citing works

A systematic evaluation of normalization methods in quantitative label-free proteomics

Välikangas, T., Suomi, T., & Elo, L. L. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw095 · Cited by 336 · see citing works

NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis

Willforss, J., Chawade, A., & Levander, F. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00523 · Cited by 229 · see citing works

NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data

Yang, Q., Wang, Y., Zhang, Y., Li, F., Xia, W., Zhou, Y., Qiu, Y., Li, H., & Zhu, F. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa258 · Cited by 186 · see citing works

Algorithms and tools for the preprocessing of LC–MS metabolomics data

Castillo, S., Gopalacharyulu, P. V., Yetukuri, L., & Orešič, M. (2011) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2011.03.010 · Cited by 175 · see citing works

Metabolomics Data Normalization with EigenMS

Karpievitch, Y. V., Nikolic, S. B., Wilson, R., Sharman, J. E., & Edwards, L. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0116221 · Cited by 144 · see citing works

Imaging Mass Spectrometry of Three-Dimensional Cell Culture Systems

Li, H., & Hummon, A. B. (2011) · Analytical Chemistry · doi:10.1021/ac202356g · Cited by 112 · see citing works

WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis

Deng, K., Zhang, F., Tan, Q., Huang, Y., Song, W., Rong, Z., Zhu, Z., Li, K., & Li, Z. (2019) · Analytica Chimica Acta · doi:10.1016/j.aca.2019.02.010 · Cited by 63 · see citing works

Sample Preparation Strategies for Mass Spectrometry Imaging of 3D Cell Culture Models

Ahlf, D. R., Liu, X., & Hummon, A. B. (2014) · Journal of Visualized Experiments · doi:10.3791/52313 · Cited by 49 · see citing works

How (Not) to Generate a Highly Predictive Biomarker Panel Using Machine Learning

Desaire, H. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00117 · Cited by 45 · see citing works

Lipidomic comparison of 2D and 3D colon cancer cell culture models

Tobias, F., & Hummon, A. B. (2022) · Journal of Mass Spectrometry · doi:10.1002/jms.4880 · Cited by 26 · see citing works

Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference

Benedetti, E., Gerstner, N., Pučić‐Baković, M., Keser, T., Reiding, K. R., Ruhaak, L. R., Štambuk, T., Selman, M. H. J., Rudan, I., Polašek, O., et al. (2020) · Metabolites · doi:10.3390/metabo10070271 · Cited by 21 · see citing works

How to Apply Supervised Machine Learning Tools to MS Imaging Files: Case Study with Cancer Spheroids Undergoing Treatment with the Monoclonal Antibody Cetuximab

Hua, D., Liu, X., Go, E. P., Wang, Y., Hummon, A. B., & Desaire, H. (2020) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.0c00010 · Cited by 16 · see citing works

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier

Hua, D., & Desaire, H. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00066 · Cited by 12 · see citing works

Leveraging R (LevR) for fast processing of mass spectrometry data and machine learning: Applications analyzing fingerprints and glycopeptides

Pfeifer, L. D., Patabandige, M. W., & Desaire, H. (2022) · Frontiers in Analytical Science · doi:10.3389/frans.2022.961592 · Cited by 6 · see citing works

Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions

Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works

Integrative Multi-Omics and Artificial Intelligence: A New Paradigm for Systems Biology

Kant, S., Deepika, & Roy, S. (2025) · OMICS A Journal of Integrative Biology · doi:10.1177/15578100251392371 · Cited by 61 · see citing works

Machine learning for the advancement of genome-scale metabolic modeling

Kundu, P., Beura, S., Mondal, S., Das, A. K., & Ghosh, A. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108400 · Cited by 52 · see citing works

Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets

Tseng, C. Y., Salguero, J. A., Breidenbach, J. D., Solomon, E. A., Sanders, C. K., Harvey, T., Thornhill, M. G., Palmisano, S. J., Sasiene, Z. J., Blackwell, B. R., et al. (2025) · Metabolomics · doi:10.1007/s11306-025-02297-1 · Cited by 25 · see citing works

Skin Surface Sebum Analysis by ESI-MS

Isom, M., & Desaire, H. (2024) · Biomolecules · doi:10.3390/biom14070790 · Cited by 19 · see citing works

Machine Learning Framework for Ovarian Cancer Diagnostics Using Plasma Lipidomics and Metabolomics

Tokareva, A., Iurova, M. V., Starodubtseva, N. L., Chagovets, V., Novoselova, A., Kukaev, E., Евгеньевич, Ф. В., & Sukhikh, G. T. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146630 · Cited by 10 · see citing works

Groomed Fingerprint Sebum Sampling: Reproducibility and Variability According to Anatomical Collection Region and Biological Sex

Isom, M., Go, E. P., & Desaire, H. (2025) · Molecules · doi:10.3390/molecules30030726 · Cited by 7 · see citing works

Minimizing Cohort Discrepancies: A Comparative Analysis of Data Normalization Approaches in Biomarker Research

Tokareva, A., Starodubtseva, N. L., Евгеньевич, Ф. В., & Silachev, D. N. (2024) · Computation · doi:10.3390/computation12070137 · Cited by 7 · see citing works

Exploring Sample Storage Conditions for the Mass Spectrometric Analysis of Extracted Lipids from Latent Fingerprints

Chua, A. E., Go, E. P., & Desaire, H. (2025) · Biomolecules · doi:10.3390/biom15040477 · Cited by 4 · see citing works

Bayesian Neural Networks for Quantifying Uncertainty in Solute Transport Through Saturated Porous Media

Mahjour, S. K. (2025) · Processes · doi:10.3390/pr13103324 · Cited by 2 · see citing works

Multiplexed Quantification of First-Trimester Serum Biomarkers in Healthy Pregnancy

Starodubtseva, N. L., Tokareva, A., Kononikhin, A. S., Brzhozovskiy, A. G., Bugrova, A. E., Kukaev, E., Poluektova, A., Евгеньевич, Ф. В., Николаев, Е. Н., & Sukhikh, G. T. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26167970 · Cited by 1 · see citing works

Local neighbor Normalization: Reconciling accurate normalization and heterogeneity recovery in large-scale metabolomics

Lu, K., Liu, Y., Cheng, K., Guo, F., Deng, L., & Dong, J. (2025) · Analytica Chimica Acta · doi:10.1016/j.aca.2025.344440 · Cited by 1 · see citing works

The synergy of foundation technologies and computer sciences advances microbial expression systems in biomedical genetic engineering

Nguyen, L. K., Hoang, D. T., & Jouannic, S. (2025) · International review of cytology · doi:10.1016/bs.ircmb.2025.11.007

Feature Down-Selection to Improve Supervised Classification by Machine Learning on Mass Spectrometry Imaging Data

Miller, B., Chua, A. E., Isom, M., Go, E. P., Sekera, E. R., Hummon, A. B., & Desaire, H. (2026) · Molecules · doi:10.3390/molecules31122077

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

The FINESSE (Artificial Intelligence Stress Echo) study to develop and validate machine learning–based model to improve risk prediction in patients undergoing stress echocardiography for the assessment of inducible myocardial ischaemia (FINESSE Protocol)

Ihekwaba, U. K., Bennasar, M., Johnson, N., Garces, N. S., West, H., Price, B., Khoo, J. P., Squire, I. B., Chan, K., & Kardos, A. (2026) · European Heart Journal - Imaging Methods and Practice · doi:10.1093/ehjimp/qyag143

Detecting and Quantifying Variability in Mass Spectrometry-Based Metabolomics Datasets

Chua, A. E., & Desaire, H. (2026) · Biomolecules · doi:10.3390/biom16101399

Machine learning for omics data analysis.

Trabelsi, A. (2018) · doi:10.18297/etd/2911

Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets

Tseng, C. Y., Salguero, J. A., Breidenbach, J. D., Solomon, E. A., Sanders, C. K., Harvey, T., Thornhill, M. G., Palmisano, S. J., Sasiene, Z. J., Blackwell, B. R., et al. (2025) · Metabolomics · doi:10.1007/s11306-025-02297-1 · Cited by 25 · see citing works

Normalyzer: A Tool for Rapid Evaluation of Normalization Methods for Omics Data Sets

Chawade, A., Alexandersson, E., & Levander, F. (2014) · Journal of Proteome Research · doi:10.1021/pr401264n · Cited by 264 · see citing works

Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

Li, B., Tang, J., Yang, Q., Cui, X., Li, S., Chen, S., Cao, Q., Xue, W., Chen, N., & Zhu, F. (2016) · Scientific Reports · doi:10.1038/srep38881 · Cited by 173 · see citing works

Data normalization strategies in metabolomics: Current challenges, approaches, and tools

Misra, B. B. (2020) · European Journal of Mass Spectrometry · doi:10.1177/1469066720918446 · Cited by 123 · see citing works