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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
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Genomic Approach Towards Personalized Anticancer Drug Therapy
Midorikawa, Y., Tsuji, S., Takayama, T., & Aburatani, H. (2012) · Pharmacogenomics · doi:10.2217/pgs.11.157 · Cited by 10
MYCN-Dependent Expression of Sulfatase-2 Regulates Neuroblastoma Cell Survival
Solari, V., Borriello, L., Turcatel, G., Shimada, H., Sposto, R., Fernández, G. E., Asgharzadeh, S., Yates, E. A., Turnbull, J. E., & DeClerck, Y. A. (2014) · Cancer Research · doi:10.1158/0008-5472.can-13-2513 · Cited by 10
Connection Map for Compounds (CMC): A Server for Combinatorial Drug Toxicity and Efficacy Analysis
Liu, L., Tsompana, M., Wang, Y., Wu, D., Zhu, L., & Zhu, R. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00397 · Cited by 10
Social regulation of the lymph node transcriptome in rhesus macaques (Macaca mulatta)
Chun, K., Capitanio, J. P., Lamkin, D. M., Sloan, E. K., Arevalo, J. M. G., & Cole, S. W. (2016) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2016.10.029 · Cited by 10
Prognostic role of minichromosome maintenance family in multiple myeloma
Quan, L., Qian, T., Cui, L., Liu, Y., Fu, L. N., & Si, C. (2020) · Cancer Gene Therapy · doi:10.1038/s41417-020-0162-2 · Cited by 10
Genetic program activity delineates risk, relapse, and therapy responsiveness in multiple myeloma
Wall, M. A., Turkarslan, S., Wu, W., Danziger, S. A., Reiss, D. J., Mason, M. J., Dervan, A., Trotter, M. W. B., Bassett, D. E., Hershberg, R. M., et al. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00185-0 · Cited by 10
Increased Expression of SETDB1 Predicts Poor Prognosis in Multiple Myeloma
Xiang, J., Chen, X., Chen, M., & Hou, J. (2022) · BioMed Research International · doi:10.1155/2022/3307873 · Cited by 10
Sudama, G., Zhang, J., Isbister, J. D., & Willett, J. D. (2012) · Metabolomics · doi:10.1007/s11306-012-0438-0 · Cited by 10
A deep learning approach to the structural analysis of proteins
Giulini, M., & Potestio, R. (2019) · Interface Focus · doi:10.1098/rsfs.2019.0003 · Cited by 10
Chemical Structure-Based and Toxicogenomic Models
Stavitskaya, L., Aubrecht, J., & Kruhlak, N. L. (2015) · Springer eBooks · doi:10.1007/978-3-319-22084-0_2 · Cited by 10
Devanarayan, V., Donohue, M., Sperling, R. A., Johnson, K. A., Ye, Y., Charil, A., Doherty, T., Tian, L., Raman, R., Aisen, P. S. S., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70702 · Cited by 12
“56–58], the repeated cross-validation framework ensures robust performance estimates.”
Wettinger, S. B., Karađuzović-Hadžiabdić, K., Attard, R., Farrugia, R. C., Wolford, B. N., Chierici, M., Jurman, G., Alexiou, P., Peñalvo, J. L., Costa, R. S., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf526 · Cited by 9
“In the context of translational medicine or clinical applications, regulators have sought to establish best practice, such as in the MAQC and SEQC initiatives [88, 94– 96].”
Applying gene expression microarrays to pulmonary disease
Campbell, J. D., Spira, A. E., & Lenburg, M. E. (2011) · Respirology · doi:10.1111/j.1440-1843.2011.01942.x · Cited by 9
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Ryu, D., Kim, H., Joung, J., Lee, H., Bae, J. S., Kim, H., Kim, H., Park, W., & Kım, K. (2016) · Oncotarget · doi:10.18632/oncotarget.9478 · Cited by 9
Breast cancer subtype predictors revisited: from consensus to concordance?
Sontrop, H. M. J., Reinders, M. J. T., & Moerland, P. D. (2016) · BMC Medical Genomics · doi:10.1186/s12920-016-0185-6 · Cited by 9
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Feng, X., Wang, E., & Cui, Q. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00156 · Cited by 9
Raybould, A. F., Holt, K., & Kimber, I. (2019) · GM crops & food · doi:10.1080/21645698.2019.1621615 · Cited by 9
A 5-Gene Stemness Score for Rapid Determination of Risk in Multiple Myeloma
Bai, H., & Chen, B. (2020) · OncoTargets and Therapy · doi:10.2147/ott.s249895 · Cited by 8
Zhao, Y. C., Zhou, Y., Liu, Y., Hao, Y., Li, M., Pu, X., Li, C., & Wen, Z. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03544-z · Cited by 8
Ban, C., Yang, F., Wei, M., Liu, Q., Wang, J., Chen, L., Lu, L., Xie, D., Liu, L., & Huang, J. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.666561 · Cited by 8
The effects of multiple probes on the hybridization of target DNA on surfaces
Welling, R. C., & Knotts, T. A. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4904929 · Cited by 9
Chan, J. Y., Kinsella, M. T., Willis, J. E., Hu, H., Reynolds, H. L., Delaney, C. P., McCulla, A., Deharo, S., Ahdesmaki, M. J., Allen, W. L., et al. (2013) · Frontiers in Oncology · doi:10.3389/fonc.2013.00288 · Cited by 9
Quantitative knowledge-based analysis in compound safety assessment
Bureeva, S. V., & Nikolsky, Y. (2011) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1517/17425255.2011.553191 · Cited by 9
NETBAGs: A Network-Based Clustering Approach with Gene Signatures for Cancer Subtyping Analysis
Wu, L., Liu, Z., Xu, J., Chen, M., Fang, H., Tong, W., & Xiao, W. (2015) · Biomarkers in Medicine · doi:10.2217/bmm.15.96 · Cited by 9
Initiative, A. D. N., Blum, D., Liepelt‐Scarfone, I., Berg, D., Gasser, T., la Fougère, C. J., & Reimold, M. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04400-w · Cited by 9
Production of Ethanol from Hardwood
Mitchell, J. (2006) · DigitalCommons (California Polytechnic State University) · Cited by 9
EATRIS-Plus Multi-omics working group and Stakeholders, Alonso-Andrés, P., Baldazzi, D., Chen, Q., Conde, E., Crespo-Toro, L., Donner, K. M., Džubák, P., Ekberg, S., García‐Bermejo, L., et al. (2025) · Phenomics · doi:10.1007/s43657-024-00170-0 · Cited by 14
“Largely depending on the research or clinical question and the available material, extraction methods may vary, and many technical and statistical approaches may be employed, sometimes leading to tremendous discrepancies in the results (Shi et al. 2010).”
Gao, X., Yourick, M. R., Campasino, K., Zhao, Y., Sepehr, E., Vaught, C. A., Sprando, R. L., & Yourick, J. J. (2025) · BMC Genomics · doi:10.1186/s12864-025-11548-3 · Cited by 8
“…use hybridization-based approach to profile transcriptome-wide gene expression by measuring fluorescence intensity of predefined transcripts, and have the merits of relatively simple sample preparation, low per sample cost, and well-established methodologies of data processing and analysis [6].”
Five more years of Nature Biotechnology research
Baker, M., & DeFrancesco, L. (2011) · Nature Biotechnology · doi:10.1038/nbt.1798 · Cited by 8
Uncertainty estimation with a finite dataset in the assessment of classification models
Chen, W., Yousef, W. A., Gallas, B. D., Hsu, E. R., Lababidi, S., Tang, R., Pennello, G. A., Symmans, W. F., & Pusztai, L. (2011) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2011.05.024 · Cited by 8
“In the pilot-study stage, the final model and features were assessed by an independent dataset that was collected as part of the MAQC-II project.”
InfluentialReliable and Sample Saving Gene Expression Analysis Approach for Diagnostic Tool Development
Port, M., Seidl, C., Ruf, C. G., Riecke, A., Meineke, V., & Abend, M. (2012) · Health Physics · doi:10.1097/hp.0b013e31824ac318 · Cited by 8
“Increased sensitivity might be reached when searching for DEG with lower fold-changes (Deng et al. 2009), but this requires multiple microarray experiments and complex statistics, while biological significance stays questionable (Shi et al. 2010; Parry et al. 2010).”
BackgroundExploiting gene dependency to inform drug development for multiple myeloma
Went, M., Hoang, P. H., Law, P., Kaiser, M., & Houlston, R. S. (2022) · Scientific Reports · doi:10.1038/s41598-022-16940-7 · Cited by 8
Acs, M., Acs, R., Briandi, C., Eubanks, E., Rehman, O., & Zhuang, H. (2023) · IEEE Access · doi:10.1109/access.2023.3280066 · Cited by 8
Mackiewicz, J., Tomczak, J., Lisek, M., Sakowicz, A., Guo, F., & Boczek, T. (2024) · Molecular Neurobiology · doi:10.1007/s12035-024-04129-0 · Cited by 9
“Two Micro-array Quality Control studies demonstrated that a P-value calculation based on fold change could be considered sufficient for obtaining reproducible results across microarray analyses, including RT 2 Profiler PCR Arrays [ 57, 58].”
MethodLauss, M., Frigyesi, A., Rydèn, T., & Höglund, M. (2010) · BMC Cancer · doi:10.1186/1471-2407-10-532 · Cited by 8
Xu, J., Jing, R., Liu, Y., Dong, Y., Wen, Z., & Li, M. (2016) · Scientific Reports · doi:10.1038/srep28720 · Cited by 8
Nishihori, T., & Shain, K. H. (2017) · International Journal of Genomics · doi:10.1155/2017/6934183 · Cited by 9
“The gene expression microarray has become the most commonly used technology for genomic investigation in oncology which is also robust and has good interlaboratory agreement [36].”
BackgroundPrediction of early breast cancer patient survival using ensembles of hypoxia signatures
Gong, I. Y., Fox, N. S., Huang, V., & Boutros, P. C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204123 · Cited by 8
“Some groups have suggested that different preprocessing methods have minor effect on predictive signatures [42, 43].”
BackgroundRevisiting inconsistency in large pharmacogenomic studies
Safikhani, Z., Smirnov, P., Freeman, M., El-Hachem, N., She, A., Rene, Q., Goldenberg, A., Birkbak, N. J., Hatzis, C., Shi, L. M., et al. (2017) · F1000Research · doi:10.12688/f1000research.9611.2 · Cited by 8
Protein expression of the gp78 E3 ligase predicts poor breast cancer outcome based on race
Singhal, S. K., Byun, J. S., Yan, T., Yancey, R., Caban, A., Gil‐Hernández, S., Bufford, S., Hewitt, S. M., Winfield, J. R., Pradhan, J. S., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.157465 · Cited by 8
Binyamin, H., & Senderowitz, H. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00823-6 · Cited by 8
Maneix, L., Iakova, P. A., Moree, S. E., Hsu, J. I., Mistry, R. M., Stossi, F., Lulla, P., Sun, Z., Sahin, E., Yellapragada, S. V., et al. (2022) · Cancer Research Communications · doi:10.1158/2767-9764.crc-22-0255 · Cited by 8
Özcan, G. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1216438 · Cited by 8
Akiyama, H., Ueda, Y., Nobumasa, H., Ooshima, H., Ishizawa, Y., Kitahiro, K., Miyagawa, I., Watanabe, K., Nakamura, T., Tanaka, R., et al. (2014) · Analytical Biochemistry · doi:10.1016/j.ab.2014.11.012 · Cited by 8
Garcia-Gomez, J. M., Gómez‐Sanchís, J., Escandell-Montero, P., Fuster‐García, E., & Soria‐Olivas, E. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.08.025 · Cited by 8
Bosio, M., Bellot, P., Salembier, P., & Oliveras, A. (2012) · Journal of Biological Systems · doi:10.1142/s0218339012400025 · Cited by 8
Improving Ribosomal RNA Integrity in Surgically Resected Human Brain Tumor Biopsies
Domingo, X. C., Ferrer‐Font, L., Dávila, M., Candiota, A. P., Simoes, R. V., Coello, A. F., Gabarrós, A., Boluda, S., Barceló, A., Ariño, J., et al. (2016) · Biopreservation and Biobanking · doi:10.1089/bio.2015.0086 · Cited by 8
Comparing continuous and discrete analyses of breast cancer survival information
Bhandari, V., & Boutros, P. C. (2016) · Genomics · doi:10.1016/j.ygeno.2016.06.002 · Cited by 8
Proteomics and Precision Medicine
Ding, C., Qin, Z., Li, Y., Shi, W., Li, L., Zhan, D., & Yang, W. (2019) · Small Methods · doi:10.1002/smtd.201900075 · Cited by 9
Devesa-Peiró, A., Sánchez-Reyes, J. M., & Díaz-Gimeno, P. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816561-4.00004-1 · Cited by 8
Gene Expression Profiling in Non-Hodgkin Lymphomas
Song, J. Y., Yu, J., & Chan, W. C. (2015) · Cancer treatment and research · doi:10.1007/978-3-319-13150-4_4 · Cited by 8
“With the advent of microarrays commercially manufactured under strictly controlled conditions and experiments performed according to established procedures, array experiments are highly reproducible when performed on the same platform and even results on different platforms at multiple large centers have given highly…”
BackgroundWang, H., Huang, L., Jing, R., Yang, Y., Liu, K., Li, M., & Wen, Z. (2015) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2015.07.004 · Cited by 8
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Joshi, K. D., & Surgenor, B. W. (2018) · International Conference on Mechatronics and Machine Vision in Practice (M2VIP) · doi:10.1109/m2vip.2018.8600819 · Cited by 8
Dancik, G. M., & Theodorescu, D. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809939-1.00035-7 · Cited by 8
Cheng, J., Greshock, J. D., Shi, L. M., Zheng, S. S., Menius, A., & Lee, K. (2013) · BMC Systems Biology · doi:10.1186/1752-0509-7-s4-s2 · Cited by 8
A Leisurely Look at Versions and Variants of the Cross Validation EstimatorPreprint
Yousef, W. A. (2019) · arXiv (Cornell University) · Cited by 8
Recent Advances in Drug Discovery and Cancer Diagnoses
Zhang, J., & Chai, H. (2020) · Current Topics in Medicinal Chemistry · doi:10.2174/156802662021200817164143 · Cited by 8
Yu, Y., Hou, W., Wang, H., Dong, L., Liu, Y., Sun, S., Yang, J., Cao, Z., Zhang, P., Zi, Y., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.26.507265 · Cited by 8
Devanarayan, V., Horie, K., Wildsmith, K. R., Doherty, T., Andreozzi, E. M., Charil, A., Sachdev, P., Kramer, L. D., Dhadda, S., & Irizarry, M. C. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70881 · Cited by 7
Li, J., Arsang‐Jang, S., Cheng, Y., Sun, F., D’Souza, A., Dhakal, B., Hari, P., Huang, Q., Auer, P. L., Li, Y., et al. (2024) · Blood Cancer Journal · doi:10.1038/s41408-024-01024-8 · Cited by 7
Ning, L., & Xie, N. (2024) · Cancer Management and Research · doi:10.2147/cmar.s445248 · Cited by 7
Dorff, K. C., Chambwe, N., Srdanovic, M., & Campagne, F. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq463 · Cited by 7
Computational Network Analysis for Drug Toxicity Prediction
Hardt, C., Bauer, C., Schuchhardt, J., & Herwig, R. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8618-7_16 · Cited by 7
PLP2 Expression as a Prognostic and Therapeutic Indicator in High‐Risk Multiple Myeloma
Bai, H., Zhu, Y., Xu, P., & Chen, B. (2020) · BioMed Research International · doi:10.1155/2020/4286101 · Cited by 7
“We also applied the Kaplan-Meier analysis to validated PLP2 gene expression in another independent dataset, and the Kaplan-Meier survival analysis suggested that patients in the PLP2low subgroup had better OS and PFS compared with those in the PLP2high subgroup in GSE24080 (p = 0:0335, Figure 3(c); p = 0:0473, Figure…”
MethodInfluentialLi, T., Yao, L., Yin, H., & Wu, Q. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1100170 · Cited by 7
“GSE24080, GSE4581 and GSE2658 were obtained using the GPL570 platform (Hanamura et al., 2006; Shi et al., 2010).”
MethodApplication of DNA Microarray Technology to Gerontological Studies
Masuda, K., Kuwano, Y., Nishida, K., & Rokutan, K. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-556-9_19 · Cited by 7
Lacroix, B., & Citovsky, V. (2013) · F1000Research · doi:10.12688/f1000research.2-33.v1 · Cited by 7
Shifting from Population-wide to Personalized Cancer Prognosis with Microarrays
Shao, L., Fan, X., Cheng, N., Wu, L., Xiong, H., Fang, H., Ding, D., Shi, L. M., Cheng, Y., & Tong, W. (2012) · PLoS ONE · doi:10.1371/journal.pone.0029534 · Cited by 7
Azuaje, F. J., Zheng, H., Camargo, A. V., & Wang, H. (2011) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.02.003 · Cited by 7
Jiang, L., Huang, L., Kuang, Q., Zhang, J., Li, M., Wen, Z., & He, L. F. (2014) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2013.12.002 · Cited by 7
Language workbench user interfaces for data analysis
Benson, V., & Campagne, F. (2015) · PeerJ · doi:10.7717/peerj.800 · Cited by 7
“We presented62 an introduction to the biomarker development and validation process in Deng and Campagne (2010).63 Similar descriptions are also available in the MAQC-II articles Shi et al. (2010).”
MethodXu, L., Cheng, C., George, E. O., & Homayouni, R. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-s8-s23 · Cited by 7
“Although the completion of the Microarray Quality Control (MAQC) project provides a framework to assess microarray technologies, others have pointed out that it does not sufficiently address inter- and intra-platform comparability and reproducibility [3-5].”
BackgroundTang, S., Hemberg, M., Cansızoğlu, A. E., Belin, S., Kosik, K. S., Kreiman, G., Steen, H., & Steen, J. A. J. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw157 · Cited by 7
Grossman, I., Kolitz, S. E., Komlosh, A., Zeskind, B. J., Weinstein, V., Laifenfeld, D., Gilbert, A., Bar‐Ilan, O., Fowler, K. D., Hasson, T. H., et al. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13547 · Cited by 7
Bead-based RNA multiplex panels for biomarker detection in oncology samples
Scerri, J., Baldacchino, S., Saliba, C., Scerri, C. A., & Grech, G. (2018) · Methods · doi:10.1016/j.ymeth.2018.10.008 · Cited by 7
Joint Estimation of the Two-Level Gaussian Graphical Models Across Multiple Classes
Shan, L., Qiao, Z., Cheng, L., & Kim, I. (2019) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2019.1694522 · Cited by 7
“The human breast cancer data set was collected from the University of Texas M.D. Anderson Cancer Center (Shi et al. 2010), which contains 22,283 gene expression measurements across 176 white patients and 102 nonwhite patients.”
MethodOptimizing use of multi-antibody assays for Lyme disease diagnosis: A bioinformatic approach
Porwancher, R. B., & Landsberg, L. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253514 · Cited by 7
“Better understanding of individual biochemical or gene transcription responses in disease pathogenesis may help narrow selection of diagnostic targets, aiding standardization [88].”
BackgroundGonçalvès, A., Moretta, J., Eisinger, F., & Bertucci, F. (2013) · Bulletin du Cancer · doi:10.1684/bdc.2013.1856 · Cited by 7
Assessing the Clinical Relevance of Oncogenic Pathways in Neoadjuvant Breast Cancer
Verderio, P. (2012) · Journal of Clinical Oncology · doi:10.1200/jco.2012.41.7386 · Cited by 7
Nonparametric empirical Bayes and maximum likelihood estimation for high-dimensional data analysisPreprint
Dicker, L. H., & Zhao, S. D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.2635 · Cited by 7
Matlab vs. OpenCV: A Comparative Study of Different Machine Learning AlgorithmsPreprint
Elsayed, A. A., & Yousef, W. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.01213 · Cited by 7
“…(Yousef et al., 2006; Chen et al., 2012a), uncertainty estimation using cross validation estimator (Yousef, 2019b,c; Yousef and Chen, 2009), and the best practices for classifier design and assessment Chen et al. (2012b); Shi et al. (2010); Yousef and Kundu (2014); Yousef (2019d), among many others.”
MethodA Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep LearningPreprint
Clyde, A., Brettin, T., Partin, A., Shaulik, M., Yoo, H., Evrard, Y. A., Zhu, Y., Xia, F., & Stevens, R. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.00095 · Cited by 7
Shang, J., He, J., Zhao, Y., Yang, J., Lin, Y., Zhang, N., Ren, L., Chen, Q., Yu, Y., Shi, L. M., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06316-6 · Cited by 6
“The small number of genes (features) and the over-fitting-resistant process by which how such genes are selected are two critically important characteristics for the robustness of a predictive model [10].”
A centromere-associated gene score for rapid determination of risk in multiple myeloma.
Bai, H., Xu, P., Wang, L., & Chen, B. (2020) · PubMed Central · Cited by 6
Molecular Signatures of CB-6644 Inhibition of the RUVBL1/2 Complex in Multiple Myeloma
Yi, W., Dziadowicz, S. A., Mangano, R. S., Wang, L., McBee, J. C., Frisch, S. M., Hazlehurst, L., Adjeroh, D., & Hu, G. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25169022 · Cited by 6
“The initial analysis covered three cohorts of newly diagnosed (ND) MM patients: the CoMMpass clinic trial (IA14) [32], MAPQ-II [33], and TT2 [34].”
MethodEstimating classification probabilities in high-dimensional diagnostic studies
Appel, I. J., Gronwald, W., & Spang, R. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr434 · Cited by 6
Jing, R., Liang, Y., Yi, R., Feng, S., Wei, Y., & He, L. F. (2018) · International Journal of Genomics · doi:10.1155/2018/8124950 · Cited by 6
A Machine Learning Pipeline for Discriminant Pathways Identification
Barla, A., Jurman, G., Visintainer, R., Squillario, M., Filosi, M., Riccadonna, S., & Furlanello, C. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35686-5_4 · Cited by 6
Zhou, X., Liu, J., & Xiong, J. (2012) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2012.6392717 · Cited by 6
Influence of genomics on adjuvant treatments for pre-invasive and invasive breast cancer
Abu-Khalaf, M. M., & Pusztai, L. (2013) · The Breast · doi:10.1016/j.breast.2013.07.015 · Cited by 6
Zou, C., Bonte, L. R. L., Pavlov, V. I., & Stahl, G. L. (2012) · Frontiers in Immunology · doi:10.3389/fimmu.2012.00015 · Cited by 6
Introduction to Toxicogenomics-Based Cellular Models
Kleinjans, J. C. S. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-397862-2.00001-2 · Cited by 6
Transcriptomics of the Human Endometrium and Embryo Implantation
Miravet-Valenciano, J., Ruíz-Alonso, M., & Simón, C. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813570-9.00012-7 · Cited by 6
Ensemble based biomarker identification on pancreatic ductal adenocarcinoma gene expressions
Pahari, P., Basak, P., & Sarkar, A. (2019) · International Journal of Computers and Applications · doi:10.1080/1206212x.2019.1636524 · Cited by 6
“Novianti et all manage the hidden many-sided quality of the information, and additionally in the system use to manufacture the order [18,19] demonstrate.”
BackgroundZhu, F., Wang, X., Ye, Z., Gan, Z., & Lai, Y. (2020) · Molecular Medicine Reports · doi:10.3892/mmr.2020.10930 · Cited by 6
Penalized estimation of the Gaussian graphical model from data with replicates
van Wieringen, W. N., & Chen, Y. (2021) · Statistics in Medicine · doi:10.1002/sim.9028 · Cited by 5
Nonparametric false discovery rate control for identifying simultaneous signals
Zhao, S. D., & Nguyen, Y. (2020) · Electronic Journal of Statistics · doi:10.1214/19-ejs1663 · Cited by 6
Boareto, M., & Caticha, N. (2014) · Microarrays · doi:10.3390/microarrays3040340 · Cited by 6
Multi-omics Quality Assessment in Personalized Medicine through EATRISPreprint
EATRIS Plus Multi-omics working group and stakeholders (in alphabetical order by last name), Alonso-Andrés, P., Baldazzi, D., Chen, Q., Conde, E., Crespo-Toro, L., Donner, K. M., Džubák, P., Ekberg, S., García‐Bermejo, L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.25.563912 · Cited by 6
The bone marrow immune ecosystem shapes daratumumab acquired resistance in plasma cell myeloma
Wang, Y., Chen, S., Liang, Z., Gale, R. P., Liu, S., Chen, X., Chi, P., Song, Y., Zhang, Y., Wang, W., et al. (2025) · Leukemia · doi:10.1038/s41375-025-02712-5 · Cited by 5
“After QC, 92,472 cells were analyzed using Seurat and UMAP clustering, identifying seven major immune cell types: (1) CD8-positive T-cells; (2) CD4-positive T-cells; (3) B-cells; (4) plasma cells; (5) myeloid cells; (6) NK-/NKT-cells; and (7) cycling cells (Fig.”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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