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The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
Open accessAbstract
Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
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“Using the common intra-site DEGs to validate the called inter-site DEGs, the MCC41, 42 showed that PEER also yields a higher accuracy than the original limmavoom method (Supplementary Fig. 22d,e), indicating that global data normalization analysis methods such as PEER can also be used to improve RNA-seq data across both Illumina and the PGM platforms.”
From Detecting and correcting systematic variation in large-scale RNA sequencing data (2014) · cites this paper as [42]
In the citing paper
“for instance, the Food and Drug Administration (FDA) agency of the USA employed the MCC as the main evaluation measure in the MicroArray II / Sequencing Quality Control (MAQC/SEQC) projects [41, 42].”
From The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation (2020) · cites this paper as [41]
In the citing paper
“Within the MicroArray Quality Control (MAQC)-II study [7], we observed, however, that the performance of gene expression models in predicting clinical outcome was limited and largely dependent on the respective clinical endpoint.”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
In the citing paper
“Within the MicroArray Quality Control (MAQC)-II study [7], we observed, however, that the performance of gene expression models in predicting clinical outcome was limited and largely dependent on the respective clinical endpoint.”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
In the citing paper
“Considering the fact that the nature of the clinical endpoint strongly affects a classifier’s performance [7, 18], we included endpoints that are known to cover a broad range of predictive difficulties (low difficulty, SEX, and CLASS LABEL; intermediate difficulty, EFS ALL, and OS ALL; high difficulty, EFS HR, and OS HR).”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
In the citing paper
“Evaluation of the prediction performances in the validation set revealed that the endpoint represents the most relevant factor affecting model performances, which is well in line with the findings of the MAQC-II study [7].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [7]
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Cites
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
Dudoit, S., Fridlyand, J., & Speed, T. P. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502753479248 · Cited by 2,671 · cited 1×
“Although previous studies have compared and benchmarked individual steps in the model development proces”
ResultMAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126
Self-citationThat BLUP is a Good Thing: The Estimation of Random Effects
Robinson, G. K. (1991) · Statistical Science · doi:10.1214/ss/1177011926 · Cited by 1,694
Selection bias in gene extraction on the basis of microarray gene-expression data
Ambroise, C., & McLachlan, G. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.102102699 · Cited by 1,537 · cited 1×
“, poor data quality and/or insufficient cross-validation of all model development step”
BackgroundDavé, S. S., Wright, G. W., Tan, B. K., Rosenwald, A., Gascoyne, R. D., Chan, W. C., Fisher, R. I., Braziel, R. M., Rimsza, L. M., Grogan, T. M., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041869 · Cited by 1,417 · cited 1×
“Previously, reanalysis of a widely cited single stud”
BackgroundMicroarray 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
Buyse, M. E., Loi, S., Veer, L. V., Viale, G., Delorenzi, M., Glas, A. M., Saghatchian, M., Bergh, J. C. S., Lidereau, R., Ellis, P., et al. (2006) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djj329 · Cited by 1,238
Shedden, K., Taylor, J. M. G., Misek, D. E., Chang, A. C., Hanash, S., Kuick, R. D., Beer, D. G., Enkemann, S. A., Yeatman, T. J., Eschrich, S. A., et al. (2008) · Nature Medicine · doi:10.1038/nm.1790 · Cited by 1,176
Flagged first, then by citations
Cited by
Sorted by citations
Items 201–300 of 894
Predicting prognosis of breast cancer with gene signatures: are we lost in a sea of data?
Iwamoto, T., & Pusztai, L. (2010) · Genome Medicine · doi:10.1186/gm202 · Cited by 52
“Can we improve prediction through new technology platforms and improved bioinformatics tools? It seems that for certain clinical prediction problems, the currently available breast cancer gene-expression datasets may not contain enough information to be able to develop highly accurate predictors [22,23].”
BackgroundMulti-walled carbon nanotube-induced gene expression in vitro: Concordance with in vivo studies
Snyder‐Talkington, B. N., Dong, C., Zhao, X., Dymacek, J., Porter, D. W., Wolfarth, M. G., Castranova, V., Qian, Y. R., & Guo, N. L. (2014) · Toxicology · doi:10.1016/j.tox.2014.12.012 · Cited by 51
“The Ensembl database analyzes protein-coding genes (Sanna et al. 2008), while Genome Pair View (Schneider et al. 2007) and MSOAR 2.0 (Shi et al. 2010b) can predict orthologs between multiple species by assigning orthologs based on sequence similarity/positions of genes in a genome and evolutionary…”
BackgroundMethodResultInfluentialUnderstanding disease mechanisms with models of signaling pathway activities
Sebastián-León, P., Vidal, E., Mínguez, P., Conesa, A., Tarazona, S., Amadoz, A., Armero, C., Salavert, F., Vidal‐Puig, A., Montaner, D. A., et al. (2014) · BMC Systems Biology · doi:10.1186/s12918-014-0121-3 · Cited by 53
Dual roles for immune metagenes in breast cancer prognosis and therapy prediction
Alistar, A. T., Chou, J. W., Nagalla, S., Black, M. A., D¿Agostino, R., & Miller, L. D. (2014) · Genome Medicine · doi:10.1186/s13073-014-0080-8 · Cited by 52
Llano, D. A., Bundela, S., Mudar, R. A., Devanarayan, V., & Initiative, A. D. N. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182098 · Cited by 49
Traumatic brain injury induces long-lasting changes in immune and regenerative signaling
Boone, D. R., Weisz, H. A., Willey, H. E., Torres, K. E. O., Falduto, M. T., Sinha, M., Spratt, H. M., Bolding, I. J., Johnson, K. M., Parsley, M. A., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214741 · Cited by 52
Alis, D. C., Güler, A., Yergin, M., & Asmakutlu, O. (2019) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2019.10.005 · Cited by 49
BenchMetrics: a systematic benchmarking method for binary classification performance metrics
Canbek, G., Temizel, T. T., & Sağıroğlu, Ş. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06103-6 · Cited by 51
Gene expression profiles in peripheral blood for the diagnosis of autoimmune diseases
Meskó, B., Póliska, S., & Nagy, L. (2011) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2010.12.004 · Cited by 52
Multicenter assessment of shotgun metagenomics for pathogen detection
Liu, D., Zhou, H., Xu, T., Yang, Q., Mo, X., Shi, D., Ai, J., Zhang, J., Tao, Y., Wen, D., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103649 · Cited by 51
Integrating deep and radiomics features in cancer bioimaging
Bizzego, A., Bussola, N., Salvalai, D., Chierici, M., Maggio, V., Jurman, G., & Furlanello, C. (2019) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2019.8791473 · Cited by 56
Boja, E. S., & Rodriguez, H. (2011) · Annals of Laboratory Medicine · doi:10.3343/kjlm.2011.31.2.61 · Cited by 56
Hounkpe, B. W., Fiusa, M. M. L., Colella, M. P., da Costa, L. N. G., Benatti, R. O., Saad, S. T. O., Costa, F. F., Santos, M. N. N., & Paula, E. V. D. (2015) · Scientific Reports · doi:10.1038/srep17822 · Cited by 49
Ryu, D., Kim, S. J., Hong, Y., Jo, A., Kim, N., Kim, H., Lee, H., Kım, K., & Park, W. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-3174 · Cited by 47
iPSC-derived neurons as a higher-throughput readout for autism: promises and pitfalls
Prilutsky, D., Palmer, N., Smedemark-Margulies, N., Schlaeger, T. M., Margulies, D. M., & Kohane, I. S. (2013) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2013.11.004 · Cited by 51
Feature Selection based Artificial Intelligence Techniques for the Prediction of COVID like Diseases
Dutta, P., Paul, S., Obaid, A. J., Pal, S., & Mukhopadhyay, K. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1963/1/012167 · Cited by 48
“(3) (Consortium, 2010).”
High expression of UBE2T predicts poor prognosis and survival in multiple myeloma
Zhang, W., Zhang, Y., Yang, Z., Liu, X., Yang, P., Wang, J., Hu, K., He, X., Zhang, X., & Jing, H. (2019) · Cancer Gene Therapy · doi:10.1038/s41417-018-0070-x · Cited by 47
Oshi, M., Newman, S., Murthy, V., Tokumaru, Y., Yan, L., Matsuyama, R., Endo, I., & Takabe, K. (2020) · Cancers · doi:10.3390/cancers12102758 · Cited by 46
“(GSE20194; n = 248) [49] and Noguchi et al.”
Abhishek, K., & Hamarneh, G. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433782 · Cited by 45
Gregori, J., Villarreal, L., Méndez, O., Sánchez‐Pla, Á., Baselga, J. M., & Villanueva, J. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2012.05.005 · Cited by 47
“The two methods used were the batch mean centering and the inclusion in either linear model of the batch effects factor [16,24,33].”
BackgroundMethodInfluentialAmadoz, A., Sebastián-León, P., Vidal, E., Salavert, F., & Dopazo, J. (2015) · Scientific Reports · doi:10.1038/srep18494 · Cited by 47
Mellon, S. H., Wolkowitz, O. M., Schonemann, M. D., Epel, E. S., Rosser, R., Burke, H. B., Mahan, L., Reus, V. I., Stamatiou, D., Liew, C. C., et al. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.79 · Cited by 46
C/EBPβ regulates the M2 transcriptome in β-adrenergic-stimulated macrophages
Lamkin, D. M., Srivastava, S., Bradshaw, K. P., Betz, J. E., Muy, K. B., Wiese, A. M., Yee, S. K., Waggoner, R. M., Arevalo, J. M. G., Yoon, A. J., et al. (2019) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2019.05.034 · Cited by 46
Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction
Li, T., Wu, P.-Y., Phan, J. H., Hassazadeh, H., Jones, W. D., Shi, L. M., Fischer, M., Mason, C. E., Li, S., Xu, J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-74567-y · Cited by 46
“Joshua Xu5, Wei Shi10,11,12,13, Jian Wang14, Jean Thierry‑Mieg15, Danielle Thierry‑Mieg15,”
BackgroundQuality Assurance of RNA Expression Profiling in Clinical Laboratories
Tang, W., Hu, Z., Muallem, H., & Gulley, M. L. (2011) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2011.09.003 · Cited by 46
LaBreche, H. G., Nevins, J. R., & Huang, E. S. (2011) · BMC Medical Genomics · doi:10.1186/1755-8794-4-61 · Cited by 45
Is regulatory science ready for artificial intelligence?
Härtung, T., Whelan, M. P., Tong, W., & Califf, R. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01596-0 · Cited by 44
“The participants pointed out that AI models, particularly machine learning models, could generate distinct yet equally statistically accurate predictions 9 .”
Management of multiple myeloma in Asia: resource-stratified guidelines
Tan, D. C. L., Chng, W. J., Chou, T., Nawarawong, W., Hwang, S.-Y., Chim, C. S., Chen, W.-M., Durie, B. G. M., & Lee, J. H. (2013) · The Lancet Oncology · doi:10.1016/s1470-2045(13)70404-2 · Cited by 45
Piltti, K. M., Funes, G. M., Avakian, S. N., Salibian, A. A., Huang, K. I., Carta, K., Kamei, N., Flanagan, L. A., Monuki, E. S., Uchida, N., et al. (2017) · Stem Cell Reports · doi:10.1016/j.stemcr.2017.04.009 · Cited by 44
Progression signature underlies clonal evolution and dissemination of multiple myeloma
Shen, Y. J., Mishima, Y., Shi, J., Sklavenitis‐Pistofidis, R., Redd, R. A., Moschetta, M., Manier, S., Roccaro, A. M., Sacco, A., Tai, Y., et al. (2020) · Blood · doi:10.1182/blood.2020005885 · Cited by 44
The promise of graphene-based transistors for democratizing multiomics studies
Lu, H., Kane, A. A., Parkinson, J., Gao, Y., Hajian, R., Heltzen, M., Goldsmith, B. R., & Aran, K. (2021) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2021.113605 · Cited by 44
Identifying biomarkers as diagnostic tools in kidney transplantation
Mas, V. R., Mueller, T., Archer, K. J., & Maluf, D. G. (2011) · Expert Review of Molecular Diagnostics · doi:10.1586/erm.10.119 · Cited by 44
Tian, S., Zhan, D., Yu, Y., Wang, Y., Liu, M., Tan, S., Li, Y., Song, L., Qin, Z., Li, X., et al. (2023) · Genome Biology · doi:10.1186/s13059-023-03048-y · Cited by 43
Self-citationWu, L., Wáng, Y., Nie, J., Fan, X., & Cheng, Y. (2013) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2013/915343 · Cited by 43
Toward better benchmarking: challenge-based methods assessment in cancer genomics
Boutros, P. C., Margolin, A. A., Stuart, J. M., Califano, A., & Stolovitzky, G. A. (2014) · Genome biology · doi:10.1186/s13059-014-0462-7 · Cited by 42
Qin, X., Lin, L., Cao, L., Zhang, X., Song, X., Hao, J., Zhang, Y., Wei, R., Huang, X., Lu, J., et al. (2017) · Scientific Reports · doi:10.1038/srep45305 · Cited by 42
Kohonen, P. J., Benfenati, E., Bower, D., Ceder, R., Crump, M., Cross, K. P., Grafström, R. C., Healy, L. E., Helma, C., Jeliazkova, N., et al. (2013) · Molecular Informatics · doi:10.1002/minf.201200114 · Cited by 45
Iwamoto, T., Lee, J., Bianchini, G., Hubbard, R. E., Young, E. J., Matsuoka, J., Kim, S. B., Symmans, W. F., Hortobágyi, G. N., & Pusztai, L. (2011) · Breast Cancer Research and Treatment · doi:10.1007/s10549-011-1706-9 · Cited by 42
Qing, T., Yu, Y., Du, T., & Shi, L. M. (2013) · Science China Life Sciences · doi:10.1007/s11427-013-4437-9 · Cited by 45
A statistician's perspective on biomarkers in drug development
Jenkins, M. A., Flynn, A. A., Smart, T. S., Harbron, C. G., Sabin, T., Ratnayake, J., Delmar, P., Herath, A., Jarvis, P., Matcham, J., et al. (2011) · Pharmaceutical Statistics · doi:10.1002/pst.532 · Cited by 43
Yu, Y., Hou, W., Liu, Y., Wang, H., Dong, L., Mai, Y., Chen, Q., Li, Z., Sun, S., Yang, J., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01867-9 · Cited by 41
“MCC is a widely used statistic in the field of bioinformatics and machine learning, which combines test sensitivity and specificity 20,78 .”
MethodCurrent status and future prospects of toxicogenomics in drug discovery
Khan, S. R., Baghdasarian, A., Fahlman, R. P., Michail, K., & Siraki, A. G. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.11.001 · Cited by 43
Shi, W., Bessarabova, M., Dosymbekov, D., Dezső, Z., Nikolskaya, T., Dudoladova, M., Serebryiskaya, T., Bugrim, A. E., Guryanov, A., Brennan, R., et al. (2010) · The Pharmacogenomics Journal · doi:10.1038/tpj.2010.35 · Cited by 41
A strategy for evaluating pathway analysis methods
Yu, C., Woo, H. J., Yu, X., Oyama, T., Wallqvist, A., & Jaques Reifman, A. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1866-7 · Cited by 41
“gov/geo/) in February 2016 [studies of Alzheimer’s disease (AD) [15], Parkinson’s disease (PD) [16, 17], Crohn’s disease [18], influenza vaccination [19], breast cancer [20, 21], lung cancer [22, 23], burn injury [24], and trauma injury [25], and two datasets downloaded at the same time from the connectivity map proje…”
BackgroundMulti-omics integration for neuroblastoma clinical endpoint prediction
Francescatto, M., Chierici, M., Dezfooli, S. R., Zandonà, A., Jurman, G., & Furlanello, C. (2018) · Biology Direct · doi:10.1186/s13062-018-0207-8 · Cited by 41
A comparison of mechanistic signaling pathway activity analysis methods
Amadoz, A., Hidalgo, M. R., Çubuk, C., Carbonell‐Caballero, J., & Dopazo, J. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby040 · Cited by 41
“As these methodologies gain in maturity, different statistical methods were used to derive progressively reliable and robust biomarkers and gene signatures [2] that, despite not being exempt of problems [3, 4], have contributed toward increasing the knowledge on the relationships between genotypes and phenotypes and h…”
HDAC2-dependent Antipsychotic-like Effects of Chronic Treatment with the HDAC Inhibitor SAHA in Mice
Revenga, M. D. L. F., Ibi, D., Saunders, J. M., Cuddy, T., Ijaz, M. K., Toneatti, R., Kurita, M., Holloway, T. D., Shen, L., Seto, J., et al. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2018.07.010 · Cited by 39
Oshi, M., Gandhi, S., Huyser, M. R., Tokumaru, Y., Yan, L., Yamada, A., Matsuyama, R., Endo, I., & Takabe, K. (2021) · PubMed Central · Cited by 41
Schilcher, J., Nilsson, A., Andlid, O., & Eklund, A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107704 · Cited by 41
Systems-Based Approaches to Cardiovascular Biomarker Discovery
Azuaje, F. J., Dewey, F. E., Brutsaert, D. L., Devaux, Y., Ashley, E. A., & Wagner, D. R. (2012) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.112.962977 · Cited by 44
Enhanced expression of FCER1G predicts positive prognosis in multiple myeloma
Fu, L. N., Cheng, Z., Dong, F., Quan, L., Cui, L., Liu, Y., Zeng, T., Huang, W., Chen, J., Pang, Y. K., et al. (2019) · Journal of Cancer · doi:10.7150/jca.37313 · Cited by 40
The path to routine use of genomic biomarkers in the cancer clinicPreprint
Boutros, P. C. (2015) · Genome Research · doi:10.1101/gr.191114.115 · Cited by 41
“For example, several studies of microarray processing techniques have yielded discordant results (Shedden et al. 2005; Shi et al. 2005, 2006, 2010; Canales et al. 2006; Zhu et al. 2010).”
Genomic Biomarkers for Personalized Medicine: Development and Validation in Clinical Studies
Matsui, S. (2013) · Computational and Mathematical Methods in Medicine · doi:10.1155/2013/865980 · Cited by 41
Muhammad, S. A., Raza, W., Nguyen, T. M., Bai, B., Wu, X., & Chen, J. Y. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00013 · Cited by 39
Goetz, A. K., Singh, B., Battalora, M., Breier, J. M., Bailey, J. P., Chukwudebe, A. C., & Janus, E. R. (2011) · Regulatory Toxicology and Pharmacology · doi:10.1016/j.yrtph.2011.07.012 · Cited by 40
Analysis of the transcriptome in molecular epidemiology studies
McHale, C. M., Zhang, L., Thomas, R., & Smith, M. T. (2013) · Environmental and Molecular Mutagenesis · doi:10.1002/em.21798 · Cited by 38
Gómez‐Carballa, A., Cebey‐López, M., Pardo‐Seco, J., Barral‐Arca, R., Calle, I. R., Pischedda, S., Currás-Tuala, M. J., Rial, J. G., Barros, F., Martinón‐Torres, F., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-48162-9 · Cited by 37
Gandhi, S., Oshi, M., Murthy, V., Repasky, E. A., & Takabe, K. (2021) · Cancers · doi:10.3390/cancers13112559 · Cited by 38
Castro‐Ferreira, M. P., de Boer, T. E., Colbourne, J. K., Vooijs, R., van Gestel, C. A. M., van Straalen, N. M., Soares, A. M. V. M., Amorim, M. J. B., & Roelofs, D. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-302 · Cited by 38
The Journey to Regulation of Protein-Based Multiplex Quantitative Assays
Boja, E. S., Jortani, S. A., Ritchie, J., Hoofnagle, A. N., Težak, Ž., Mansfield, E. A., Keller, P., Rivers, R. C., Rahbar, A. M., Anderson, N. L., et al. (2011) · Clinical Chemistry · doi:10.1373/clinchem.2010.156034 · Cited by 37
Boone, D. R., Micci, M., Taglialatela, I. G., Hellmich, J. L., Weisz, H. A., Bi, M., Prough, D. S., DeWitt, D. S., & Hellmich, H. L. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127287 · Cited by 36
Zhao, Y., Schaafsma, E., & Cheng, C. (2020) · Cancer Medicine · doi:10.1002/cam4.3284 · Cited by 37
Ferté, C., Trister, A. D., Huang, E. S., Bot, B. M., Guinney, J., Commo, F., Sieberts, S. K., André, F., Besse, B., Soria, J., et al. (2013) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-12-3937 · Cited by 36
Viral Immunogenicity Prediction by Machine Learning Methods
Doneva, N., & Dimitrov, I. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25052949 · Cited by 38
Orthogonal PLS (OPLS) Modeling for Improved Analysis and Interpretation in Drug Design
Eriksson, L., Rosén, J., Johansson, E., & Trygg, J. (2012) · Molecular Informatics · doi:10.1002/minf.201200158 · Cited by 36
MEL-18 loss mediates estrogen receptor–α downregulation and hormone independence
Lee, J., Won, H., Park, J., Kim, H., Choi, H., Shin, D., Kang, J., Woo, J. K., Oh, S.-H., Son, T., et al. (2015) · Journal of Clinical Investigation · doi:10.1172/jci73743 · Cited by 36
Wu, R., Patel, A. P., Tokumaru, Y., Asaoka, M., Oshi, M., Yan, L., Ishikawa, T., & Takabe, K. (2022) · Breast Cancer Research and Treatment · doi:10.1007/s10549-022-06552-0 · Cited by 36
Predictability of drug-induced liver injury by machine learning
Chierici, M., Francescatto, M., Bussola, N., Jurman, G., & Furlanello, C. (2020) · Biology Direct · doi:10.1186/s13062-020-0259-4 · Cited by 35
Transcriptional Profiling to Identify Biomarkers of Disease and Drug Response
Mendrick, D. L. (2011) · Pharmacogenomics · doi:10.2217/pgs.10.184 · Cited by 36
Gong, B., Li, D., Kusko, R. L., Novoradovskaya, N., Zhang, Y., Wang, S., Pabón-Peña, C., Zhang, Z., Lai, K., Cai, W., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02315-0 · Cited by 38
Más-o-menos: a simple sign averaging method for discrimination in genomic data analysis
Zhao, S. D., Parmigiani, G., Huttenhower, C., & Waldron, L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu488 · Cited by 35
Revealing the missing expressed genes beyond the human reference genome by RNA-Seq
Chen, G., Li, R., Shi, L. M., Qi, J., Hu, P., Luo, J., Liu, M., & Shi, T. (2011) · BMC Genomics · doi:10.1186/1471-2164-12-590 · Cited by 33
High-dimensional classification via nonparametric empirical Bayes and maximum likelihood inference
Dicker, L. H., & Zhao, S. D. (2016) · Biometrika · doi:10.1093/biomet/asv067 · Cited by 34
Talhouk, A., Kommoss, S., Mackenzie, R., Cheung, M., Leung, S. C. Y., Chiu, D. S., Kalloger, S. E., Huntsman, D. D., Chen, S., Intermaggio, M. P., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153844 · Cited by 37
Asensio, M. A., Lim, Y. W., Wayham, N. P., Stadtmiller, K., Edgar, R. C., Leong, J., Leong, R., Mizrahi, R. A., Adams, M. S., Simons, J. F., et al. (2019) · mAbs · doi:10.1080/19420862.2019.1583995 · Cited by 34
A merged lung cancer transcriptome dataset for clinical predictive modeling
Lim, S. B., Tan, S. J., Lim, W., & Lim, C. T. (2018) · Scientific Data · doi:10.1038/sdata.2018.136 · Cited by 36
Zhuang, D., & Ibrahim, A. K. (2021) · Applied Sciences · doi:10.3390/app11177772 · Cited by 33
Assessment of pharmacogenomic agreement
Safikhani, Z., El-Hachem, N., Quevedo, R., Smirnov, P., Goldenberg, A., Birkbak, N. J., Mason, C. E., Hatzis, C., Shi, L. M., Aerts, H. J. W. L., et al. (2016) · F1000Research · doi:10.12688/f1000research.8705.1 · Cited by 31
Bone Marrow Stroma-Induced Transcriptome and Regulome Signatures of Multiple Myeloma
Dziadowicz, S. A., Wang, L., Akhter, H., Aesoph, D., Sharma, T., Adjeroh, D., Hazlehurst, L., & Hu, G. (2022) · Cancers · doi:10.3390/cancers14040927 · Cited by 33
Biomarkers of Coronary Artery Disease: The Promise of the Transcriptome
Siemelink, M. A., & Zeller, T. (2014) · Current Cardiology Reports · doi:10.1007/s11886-014-0513-4 · Cited by 34
Cheng, L., Wang, X., Wong, P.-K., Lee, K.-Y., Li, L., Xu, B., Wang, D., & Leung, K. (2016) · Molecular BioSystems · doi:10.1039/c6mb00386a · Cited by 33
Interoperable RNA-Seq analysis in the cloud
Lachmann, A., Clarke, D. J. B., Torre, D., Xie, Z., & Ma’ayan, A. (2020) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2020.194521 · Cited by 33
Chen, G., Yin, K., Shi, L. M., Fang, Y., Qi, Y., Li, P., Luo, J., He, B., Liu, M., & Shi, T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028318 · Cited by 33
Parola, C., Neumeier, D., Friedensohn, S., Csepregi, L., Tacchio, M. D., Mason, D. M., & Reddy, S. T. (2019) · mAbs · doi:10.1080/19420862.2019.1662691 · Cited by 33
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Wang, H., Chen, C.-H., Shi, S., Chung, C., Wen, Y.-H., Wu, M., Lebowitz, M., Zhou, J., & Lu, J. (2020) · Cancers · doi:10.3390/cancers12061442 · Cited by 32
Camillo, B. D., Sanavia, T., Martini, M., Jurman, G., Sambo, F., Barla, A., Squillario, M., Furlanello, C., TOFFOLO, G. M., & Cobelli, C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032200 · Cited by 33
SIGNATURE: A workbench for gene expression signature analysis
Chang, J. T., Gatza, M. L., Lucas, J. E., Barry, W. T., Vaughn, P., & Nevins, J. R. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-443 · Cited by 32
Borup, R., Thuesen, L. L., Andersen, C. Y., Andersen, A. N., Ziebe, S., Winther, O., & Grøndahl, M. L. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153562 · Cited by 32
“This is one of the conclusions of a recent survey of hundreds of classification studies which showed that, despite of large dissimilarities between gene signatures depending on the choice of sample pre-processing and high level data analysis, the underlying biology of the different signatures in various classifiers wh…”
Lagani, V., Karozou, A. D., Gómez-Cabrero, D., Silberberg, G., & Tsamardinos, I. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1038-1 · Cited by 32
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Devanarayan, V., Ye, Y., Charil, A., Andreozzi, E. M., Sachdev, P., Llano, D. A., Tian, L., Zhu, L., Hampel, H. J., Kramer, L. D., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13565 · Cited by 32
Currie, R. A. (2012) · Mutation Research/Genetic Toxicology and Environmental Mutagenesis · doi:10.1016/j.mrgentox.2012.03.002 · Cited by 31
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Shi, L. M., Kusko, R. L., Wolfinger, R. D., Haibe‐Kains, B., Fischer, M., Sansone, S., Mason, C. E., Furlanello, C., Jones, W. D., Ning, B., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.4029 · Cited by 31
Zhang, S., Xie, Y., Tian, T., Yang, Q., Zhou, Y., Qiu, J., Xu, L., Wen, N., Lv, Q., & Du, Z. (2021) · Oncology Letters · doi:10.3892/ol.2021.12671 · Cited by 31
Transcriptomic profiling of rat liver samples in a comprehensive study design by RNA-Seq
Gong, B., Wang, C., Su, Z., Hong, H., Thierry‐Mieg, J., Thierry‐Mieg, D., Shi, L. M., Auerbach, S. S., Tong, W., & Xu, J. (2014) · Scientific Data · doi:10.1038/sdata.2014.21 · Cited by 31
Taskesen, E., Babaei, S., Reinders, M. M., & de Ridder, J. (2015) · BMC Bioinformatics · doi:10.1186/1471-2105-16-s4-s5 · Cited by 30
“Over the last decade, microarray technologies that measures gene expression profiles (GEP) proved to be effective for the detection of biomarkers for diagnosis and prognosis of disease and for helping with the determination of drug treatment [1].”
Binder, M., Szalat, R. E., Talluri, S., Fulciniti, M., Avet‐Loiseau, H., Parmigiani, G., Samur, M., & Munshi, N. C. (2024) · Nature Communications · doi:10.1038/s41467-024-47793-5 · Cited by 35
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Liu, Y., Ai, N., Liao, J., & Fan, X. (2015) · Biomarkers in Medicine · doi:10.2217/bmm.15.91 · Cited by 31
Schramm, A. R., Schowe, B., Fielitz, K., Heilmann, M., Martin, M., Marschall, T., Köster, J., Vandesompele, J., Vermeulen, J., Preter, K. D., et al. (2012) · British Journal of Cancer · doi:10.1038/bjc.2012.391 · Cited by 32
“International consortia such as MAQC have defined standard operating procedures for validating microarray data (Shi et al, 2010).”
MethodMinePath: Mining for Phenotype Differential Sub-paths in Molecular Pathways
Koumakis, L., Kanterakis, A., Kartsaki, E., Chatzimina, M., Zervakis, M., Tsiknakis, M., Vassou, D., Kafetzopoulos, D., Kostas Marias, A., Moustakis, V. S., et al. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005187 · Cited by 29
Zhu, F., Wang, X., Zeng, H., Yin, Z., & Ye, Z. (2019) · Oncology Letters · doi:10.3892/ol.2019.10881 · Cited by 29
Metadata
- Authors
- MAQC Consortium
- Venue
- Nature Biotechnology
- Published
- 2010-07-30
- Volume
- 28
- Issue
- 8
- Pages
- 827-838
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3315840
- OpenAlex
- W2021560851
- Cited by
- 889
- References
- 63
- Citations by year
- 2026: 13 · 2025: 33 · 2024: 39 · 2023: 35 · 2022: 40 · 2021: 58 · 2020: 61 · 2019: 58 · 2018: 46 · 2017: 35 · 2016: 57 · 2015: 54 · 2014: 87 · 2013: 85 · 2012: 79
- FWCI
- 34.94
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Institute of Process Engineering, CAS Institutional Repository (submittedVersion, cc-by-nc-sa)
- IRIS (submittedVersion)
- Repository of Futwangen University of Applied Sciences (Furtwangen University) (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Funders
- National Institutes of Health (Z99 LM999999, Z99 HL999999, Z99 MH999999, Z99CL999999)
- National Cancer Institute (CA97513-01, CA55819-01A1, R00CA126184, P01CA055819, R33CA097513)
- National Institute of General Medical Sciences (T32GM074906, R01GM083084, R01GM083226, R01GM072611)
- National Center for Research Resources (R01RR021967)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- microarray quality control, gene expression profiling, prediction model, genomic classifier, model validation, pulmonary toxicity, hepatotoxicity, breast cancer, multiple myeloma, neuroblastoma, rodent toxicity, method comparison, reproducibility
- MeSH terms
- Animals, Breast Neoplasms, Disease Models, Animal, Female, Humans, Liver Diseases, Lung Diseases, Multiple Myeloma, Neoplasms, Neuroblastoma, Predictive Value of Tests, Quality Control, Survival Analysis, Guidelines as Topic, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Rats
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