Detecting potential labeling errors in microarrays by data perturbation

Malossini, Andrea; Blanzieri, Enrico; Ng, Raymond T.

doi:10.1093/bioinformatics/btl346

Cited by 83Gene expression and cancer classificationMachine Learning and Data ClassificationGenomics and Phylogenetic Studies

The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.

Detecting potential labeling errors in microarrays by data perturbation

Open access

Malossini, Andrea, Blanzieri, Enrico, Ng, Raymond T.·2006·Bioinformatics

DOI 10.1093/bioinformatics/btl346

Open this paper in CoChat — free account Open PDF

Abstract

Motivation: Classification is widely used in medical applications. However, the quality of the classifier depends critically on the accurate labeling of the training data. But for many medical applications, labeling a sample or grading a biopsy can be subjective. Existing studies confirm this phenomenon and show that even a very small number of mislabeled samples could deeply degrade the performance of the obtained classifier, particularly when the sample size is small. The problem we address in this paper is to develop a method for automatically detecting samples that are possibly mislabeled. Results: We propose two algorithms, a classification-stability algorithm and a leave-one-out-error-sensitivity algorithm for detecting possibly mislabeled samples. For both algorithms, the key structure is the computation of the leave-one-out perturbation matrix. The classification-stability algorithm is based on measuring the stability of the label of a sample with respect to label changes of other samples and the version of this algorithm based on the support vector machine appears to be quite accurate for three real datasets. The suspect list produced by the version is of high quality. Furthermore, when human intervention is not available, the correction heuristic appears to be beneficial. Contact: malossin@dit.unitn.it

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1093/bioinformatics/btl346

Cites

24 of 24 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Human decision making can be highly irrational and subjective, especially in the medical context, representing a source of labelling noise.
“However, human decision making can be highly irrational and subjective, especially in the medical context203–205, representing one of various sources of labelling noise206.”
From The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review (2024) · cites this paper as [205]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Bagging predictors
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516
Gene selection for cancer classification using support vector machines
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002) · Machine Learning · doi:10.1023/a:1012487302797 · Cited by 9,954
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays
Alon, U. S., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S. E., Mack, D. H., & Levine, A. J. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.12.6745 · Cited by 4,170
Outliers in Statistical Data
BARNETT, V. D., Lewis, T., & Abeles, F. F. (1979) · Physics Today · doi:10.1063/1.2995737 · Cited by 2,834
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
A molecular signature of metastasis in primary solid tumors
Ramaswamy, S., Ross, K. N., Lander, E. S., & Golub, T. R. (2002) · Nature Genetics · doi:10.1038/ng1060 · Cited by 2,466
Support vector machine classification and validation of cancer tissue samples using microarray expression data
Furey, T. S., Cristianini, N., Duffy, N., Bednarski, D. W., Schummer, M., & Haussler, D. H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.10.906 · Cited by 2,457
Outliers in Statistical Data.
Atkinson, A. C., BARNETT, V. D., & Lewis, T. (1995) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2983451 · Cited by 1,693
Algorithms for Mining Distance-Based Outliers in Large Datasets
Knorr, E. M., & Ng, R. T. (1998) · Cited by 1,550
Self-citation
Predicting the clinical status of human breast cancer by using gene expression profiles
West, M., Blanchette, C., Dressman, H. K., Huang, E. S., Ishida, S., Spang, R., Zuzan, H., Olson, J. A., Marks, J. R., & Nevins, J. R. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.201162998 · Cited by 1,318
Outliers in Statistical Data.
Balakrishnan, N., Barnett, V., & Lewis, T. (1995) · Biometrics · doi:10.2307/2533352 · Cited by 1,238
Outlier detection for high dimensional data
Aggarwal, C. C., & Yu, P. S. (2001) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/375663.375668 · Cited by 1,066
Identifying Mislabeled Training Data
Brodley, C. E., & Friedl, M. A. (1999) · Cited by 718
Identifying Mislabeled Training Data
Brodley, C. E., & Friedl, M. A. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.606 · Cited by 612
Outlier detection for high dimensional data
Aggarwal, C. C., & Yu, P. S. (2001) · ACM SIGMOD Record · doi:10.1145/376284.375668 · Cited by 464
Robust principal component analysis for computer vision
de la Torre, F. N., & Black, M. J. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937541 · Cited by 387
Gene Assessment and Sample Classification for Gene Expression Data Using a Genetic Algorithm / k-nearest Neighbor Method
Li, L., Darden, T. A., Weingberg, C., Levine, A. J., & Pedersen, L. G. (2001) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207013330733 · Cited by 197
Analysis of new techniques to obtain quality training sets
Sánchez, J. S. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00225-8 · Cited by 179
Identifying and Handling Mislabelled Instances
Muhlenbach, F., Lallich, S., & Zighed, A. D. (2003) · Journal of Intelligent Information Systems · doi:10.1023/a:1025832930864 · Cited by 134
Detecting outlying samples in microarray data: A critical assessment of the effect of outliers on sample classification
Kadota, K., Tominaga, D., Akiyama, Y., & Takahashi, K. (2003) · Chem-Bio Informatics Journal · doi:10.1273/cbij.3.30 · Cited by 42
Distinguishing mislabeled data from correctly labeled data in classifier design
Venkataraman, S., Metaxas, D., Fradkin, D., Kulikowski, C. A., & Muchnik, I. B. (2005) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2004.52 · Cited by 23
Assessment of SVM Reliability for Microarray Data Analysis
Malossini, A., Blanzieri, E., & Ng, R. T. (2004) · Unitn Eprints Research (Università Degli Studi di Trento) · Cited by 7
Self-citation

Flagged first, then by citations

1 of the 25 references couldn’t be matched to a known paper.

Sorted by citations

Classification in the Presence of Label Noise: A Survey
Frenay, B., & Verleysen, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2292894 · Cited by 1,777
A Light CNN for Deep Face Representation With Noisy Labels
Wu, X., He, R., Sun, Z., & Tan, T. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2833032 · Cited by 1,243
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Karimi, D., Dou, H., Warfield, S. K., & Gholipour, A. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101759 · Cited by 768
The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review
Schwabe, D., Becker, K., Seyferth, M., Klaß, A., & Schaeffter, T. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01196-4 · Cited by 169
Improving Crowdsourced Label Quality Using Noise Correction
Zhang, J., Sheng, V. S., Li, T., & Wu, X. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2677468 · Cited by 122
Label-Noise Robust Logistic Regression and Its Applications
Bootkrajang, J., & Kabán, A. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33460-3_15 · Cited by 115
INFFC: An iterative class noise filter based on the fusion of classifiers with noise sensitivity control
Sáez, J. A., Galar, M., Luengo, J., & Herrera, F. (2015) · Information Fusion · doi:10.1016/j.inffus.2015.04.002 · Cited by 94
A Comprehensive Report on Machine Learning-based Early Detection of Alzheimer's Disease using Multi-modal Neuroimaging Data
Sharma, S., & Mandal, P. K. (2022) · ACM Computing Surveys · doi:10.1145/3492865 · Cited by 91
A Comprehensive Introduction to Label Noise
Frénay‬, B., & Kabán, A. (2014) · Cited by 77
Reducing microarray data via nonnegative matrix factorization for visualization and clustering analysis
Liu, W., Yuan, K., & Ye, D. (2007) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2007.12.003 · Cited by 65
A Simplified Cohen’s Kappa for Use in Binary Classification Data Annotation Tasks
Wang, J., Yang, Y., & Xia, B. (2019) · IEEE Access · doi:10.1109/access.2019.2953104 · Cited by 56
Noise reduction for instance-based learning with a local maximal margin approach
Segata, N., Blanzieri, E., Delany, S. J., & Cunningham, P. (2009) · Journal of Intelligent Information Systems · doi:10.1007/s10844-009-0101-z · Cited by 52
Self-citation
Learning kernel logistic regression in the presence of class label noise
Bootkrajang, J., & Kabán, A. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.05.007 · Cited by 49
mCRF and mRD: Two Classification Methods Based on a Novel Multiclass Label Noise Filtering Learning Framework
Xia, S., Chen, B., Wang, G., Zheng, Y., Gao, X., Giem, E., & Chen, Z. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3047046 · Cited by 47
Classification of mislabelled microarrays using robust sparse logistic regression
Bootkrajang, J., & Kabán, A. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt078 · Cited by 45
Personal health indexing based on medical examinations: A data mining approach
Chen, L., Li, X., Yang, Y., Kurniawati, H., Sheng, Q. Z., Hu, H., & Huang, N. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.10.008 · Cited by 44
Robust twin boosting for feature selection from high-dimensional omics data with label noise
He, S., Chen, H., Zhu, Z., Ward, D. G., Cooper, H. J., Viant, M. R., Heath, J. K., & Yao, X. (2014) · Information Sciences · doi:10.1016/j.ins.2014.08.048 · Cited by 42
On developing and enhancing plant-level disease rating systems in real fields
Atoum, Y., Afridi, M. J., Liu, X., McGrath, J. M., & Hanson, L. E. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.11.021 · Cited by 42
On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods
Sáez, J. A., Krawczyk, B., & Woźniak, M. (2016) · Applied Artificial Intelligence · doi:10.1080/08839514.2016.1193719 · Cited by 41
A Joint Detection and Recognition Approach to Lung Cancer Diagnosis From CT Images With Label Uncertainty
Liu, C., & Chan, S. C. (2020) · IEEE Access · doi:10.1109/access.2020.3044941 · Cited by 36
Simultaneous genes and training samples selection by modified particle swarm optimization for gene expression data classification
Shen, Q., Mei, Z., & Ye, B. (2009) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2009.04.008 · Cited by 36
A generalised label noise model for classification in the presence of annotation errors
Bootkrajang, J. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.12.106 · Cited by 31
Methods for labeling error detection in microarrays based on the effect of data perturbation on the regression model
Zhang, C., Wu, C., Blanzieri, E., Zhou, Y., Wang, Y., Du, W., & Liang, Y. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp478 · Cited by 30
Self-citation
Robust Logistic Regression using Shift Parameters
Tibshirani, J., & Manning, C. D. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-2021 · Cited by 29
A Survey of mislabeled training data detection techniques for pattern classification
Guan, D., & Yuan, W. (2013) · IETE Technical Review · doi:10.4103/0256-4602.125689 · Cited by 26
Learning With Auxiliary Less-Noisy Labels
Duan, Y., & Wu, O. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2546956 · Cited by 26
AN OUTLIER MAP FOR SUPPORT VECTOR MACHINE CLASSIFICATIONPreprint
Debruyne, M. (2016) · Cited by 26
An outlier map for Support Vector Machine classification
Debruyne, M. (2009) · The Annals of Applied Statistics · doi:10.1214/09-aoas256 · Cited by 25
Dealing with Noisy Data
García, S., Luengo, J., & Herrera, F. (2014) · Intelligent systems reference library · doi:10.1007/978-3-319-10247-4_5 · Cited by 24
From Weakly Supervised Learning to Biquality Learning: an Introduction
Nodet, P., Lemaire, V., Bondu, A., Cornuéjols, A., & Ouorou, A. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533353 · Cited by 23
A kernel-based approach for detecting outliers of high-dimensional biological data
Oh, J. H., & Gao, J. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s4-s7 · Cited by 23
A Scalable Noise Reduction Technique for Large Case-Based Systems
Segata, N., Blanzieri, E., & Cunningham, P. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02998-1_24 · Cited by 22
Self-citation
Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence
Wu, S., Konrad Schindler, B., Heitzler, M., & Hurni, L. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.01.021 · Cited by 21
Particle competition and cooperation for semi-supervised learning with label noise
Breve, F., Zhao, L., & Quiles, M. G. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.08.082 · Cited by 21
Modified-mean-shift-based noisy label detection for hyperspectral image classification
Bahraini, T., Azimpour, P., & Yazdi, H. S. (2021) · Computers & Geosciences · doi:10.1016/j.cageo.2021.104843 · Cited by 20
Incorporating Measurement Error in Astronomical Object Classification
Shy, S., Tak, H., Feigelson, E. D., Timlin, J. D., III, & Babu, G. J. (2022) · The Astronomical Journal · doi:10.3847/1538-3881/ac6e64 · Cited by 19
Learning to Predict Population-Level Label Distributions
Liu, T., Venkatachalam, A., Bongale, P. S., & Homan, C. M. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3317082 · Cited by 18
A flocking based data mining algorithm for detecting outliers in cancer gene expression microarray data
Bellaachia, A., & Bari, A. (2012) · International Conference on Information Retrieval & Knowledge Management (CAMP) · doi:10.1109/infrkm.2012.6204996 · Cited by 18
Novel DNA Biosensor Based on the Out-of-Equilibrium Body Potential Method in Silicon-on-Insulator
Benea, L., Popescu, M., Bawedin, M., Simion, M., & Ionica, I. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3008133 · Cited by 13
Label-Noise Resistant Logistic Regression for Functional Data Classification with an Application to Alzheimer's Disease Study
Lee, S., Shin, H., & Lee, S. H. (2016) · Biometrics · doi:10.1111/biom.12504 · Cited by 12
Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning
Haase-Schutz, C., Stal, R., Hertlein, H., & Sick, B. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9411918 · Cited by 12
A two-step anomaly detection based method for PU classification in imbalanced data sets
Vázquez, C. O., Broucke, S. V., & Weerdt, J. D. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00925-9 · Cited by 11
FaLKM-lib v1.0: A Library for Fast Local Kernel Machines
Segata, N. (2009) · Unitn Eprints Research (Università Degli Studi di Trento) · Cited by 11
An empirical study of filter-based feature selection algorithms using noisy training data
Yuan, W., Guan, D., Shen, L., & Pan, H. (2014) · IEEE International Conference on Information Science and Technology (ICIST) · doi:10.1109/icist.2014.6920367 · Cited by 9
Influence Diagnostics in Support Vector Machines
Kim, S., & Kim, C. (2020) · Journal of the Korean Statistical Society · doi:10.1007/s42952-019-00037-5 · Cited by 8
Accounting for Control Mislabeling in Case–Control Biomarker Studies
Rantalainen, M., & Holmes, C. C. (2011) · Journal of Proteome Research · doi:10.1021/pr200507b · Cited by 8
A boosting method with asymmetric mislabeling probabilities which depend on covariates
Hayashi, K. (2011) · Computational Statistics · doi:10.1007/s00180-011-0250-8 · Cited by 8
Robust Logistic Regression using Shift Parameters (Long Version)Preprint
Tibshirani, J., & Manning, C. D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.4987 · Cited by 8
Hypoxia Imaging in Lung Cancer: A PET-Based Narrative Review for Clinicians and Researchers
Alenezi, A., Alhamad, H., Alenezi, A., & Khan, M. U. H. (2025) · Pharmaceuticals · doi:10.3390/ph18040459 · Cited by 7
Label noise detection under the noise at random model with ensemble filters
de Moura, K. G., Prudêncio, R. B. C., & Cavalcanti, G. D. C. (2022) · Intelligent Data Analysis · doi:10.3233/ida-215980 · Cited by 7
Neighborhood-Based Pooling for Population-Level Label Distribution Learning
Weerasooriya, T. C., Liu, T., & Homan, C. M. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200130 · Cited by 7
Learning a Label-Noise Robust Logistic Regression: Analysis and Experiments
Bootkrajang, J., & Kabán, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41278-3_69 · Cited by 7
An Integrated Approach for Identifying Wrongly Labelled Samples When Performing Classification in Microarray Data
Leung, Y. Y., Chang, C., & Hung, Y. S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0046700 · Cited by 6
Hybrid Label Noise Correction Algorithm For Medical Auxiliary Diagnosis
Xu, J., Yang, Y., & Yang, P. (2020) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin45582.2020.9442246 · Cited by 6
Enhancing Adaboost performance in the presence of class-label noise: A comparative study on EEG-based classification of schizophrenic patients and benchmark datasets
Pouya, O. R., Boostani, R., & Sabeti, M. (2023) · Intelligent Data Analysis · doi:10.3233/ida-227125 · Cited by 5
Web Content Classification Using Distributions of Subjective Quality Evaluations
Rafalak, M., Deja, D., Wierzbicki, A., Nielek, R., & Kąkol, M. (2016) · ACM Transactions on the Web · doi:10.1145/2994132 · Cited by 5
A Multi-Granular Relative Density Model for Class Noise Detection
Liang, X., Xia, S., Liu, Q., Liu, Y., Chen, B., & Wang, G. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622383 · Cited by 5
Text Classification with Noisy Class Labels
Pagotto, A. (2020) · doi:10.22215/etd/2020-14143 · Cited by 5
Data Glitches Discovery using Influence-based Model Explanations
Myrtakis, N., Tsamardinos, I., & Christophides, V. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709285 · Cited by 4
An integrated approach to feature selection and classification for microarray data with outlier detection
Leung, Y., & Hu, Y. S. (2009) · Cited by 4
Improving Seasonal Land Cover Maps of Poyang Lake Area in China by Taking into Account Logical Transitions
Yang, G., Fang, S., Dian, Y., & Bi, C. (2016) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi5090165 · Cited by 4
A kernel SVM algorithm to detect mislabeled microarrays in human cancer samples
Martín-Merino, M. (2013) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2013.6701579 · Cited by 3
VGMTNet: A variational Gaussian mixture label transfer network for industrial fault diagnosis
Wang, W., Jiang, Q., Yan, X., & Zhong, W. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128472 · Cited by 3
Cancer classification using the Extended Biomimetic Pattern Recognition
Lee, C. H., & Truong, D. P. (2016) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2016.7758119 · Cited by 3
On the intrinsic robustness to noise of some leading classifiers and symmetric loss function -- an empirical evaluationPreprint
Baher, H. L., Lemaire, V., & Trinquart, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13570 · Cited by 3
Compensating inaccurate annotations to train 3D facial landmark localization models
Sukno, F. M., Waddington, J. L., & Whelan, P. F. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553818 · Cited by 2
A Fast Algorithm for Outlier Detection in Microarray
Zhou, Y., Xing, C., Shen, W., Sun, Y., Wu, J., & Zhou, X. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-23324-1_83 · Cited by 2
A new margin-based AdaBoost algorithm: Even more robust than RobustBoost to class-label noise
Pouya, O. R. (2016) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece.2016.7726684 · Cited by 2
Supervised learning with random labelling errors
Bootkrajang, J. (2013) · University of Birmingham Institutional Research Archive (University of Birmingham) · Cited by 2
Kernel Integration using von Neumann Entropy
Malossini, A., Segata, N., & Blanzieri, E. (2009) · Unitn Eprints Research (Università Degli Studi di Trento) · Cited by 1
Self-citation
The use of haplotype-specific transcripts improves sample annotation consistency
Hartmann, N., Luesink, E. J., Khokhlovich, E., Szustakowski, J. D., Bäriswyl, L., Peterson, J. C., Scherer, A., Nirmala, N., & Staedtler, F. (2014) · Biomarker Research · doi:10.1186/2050-7771-2-17 · Cited by 1
A Fast Relative Density Method Based on Space Partitioning
Wang, B., Xia, S., Yu, H. Z., & Wang, G. (2019) · IEEE International Conference on Big Knowledge (ICBK) · doi:10.1109/icbk.2019.00041 · Cited by 1
Improving Medical Predictions with Label Noise Tolerant Classification
Vernekar, S., Ayyar, A. B., Rajagopalan, A., Kumar, B., & Mishra, V. K. (2022) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n56670.2022.10074230 · Cited by 1
Label Quality Improvement in Crowdsourcing with Ensemble TSK Fuzzy Classifier
Zhang, X., Pan, X., & Wang, S. (2019) · International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · doi:10.1109/iske47853.2019.9170348 · Cited by 1
Combined host and microbial metagenomic next-generation sequencing: Applying integrated analysis approaches for a comprehensive evaluation of infectious disease response to inform diagnosis, surveillance, and treatment
Kalantar, K. (2019) · eScholarship (California Digital Library)
Robust Learning under Strong Noise via SQsPreprint
Anagnostides, I., Gouleakis, T., & Marashian, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09106
Common visual pattern discovery and analysis
Wang, Z. (2021) · doi:10.32657/10356/146898
Leveraging Expert Knowledge for Label Noise Mitigation in Machine Learning
Nguyen, Q., Shikina, T., Teruya, D., Hotta, S., Han, H.-D., & Nakajo, H. (2021) · Applied Sciences · doi:10.3390/app112211040
A statistical approach to quality evaluation of AI mislabel detection algorithm
Lian, J., Xie, K., Choi, K., Liu, X., Veeramani, B., Murli, S., Hu, A., Freeman, L., Bowen, E., & Deng, X. (2025) · Quality Engineering · doi:10.1080/08982112.2025.2555287
Classification et apprentissage actif à partir d'un flux de données évolutif en présence d'étiquetage incertain
Bouguelia, M.-R. (2015) · theses.fr (ABES) · doi:10.70675/378cd0fezc047z4d71zb86ez211a2af519d6
Impact du Bruit d'Annotation sur l'Évaluation de Classifieurs
Benjelloun, I. (2021) · theses.fr (ABES) · doi:10.70675/32d9112fz380ez4483zb9ebz1b9d93028852
Biquality learning : from weakly supervised learning to distribution shifts
Nodet, P. (2023) · theses.fr (ABES) · doi:10.70675/41d22fb9z3d38z4a64z9a3cz4a385058629c
A noise-tolerant Student’s t mixture discriminant analysis for industrial label-noise fault classification
Wang, W., He, Y., Jiang, Q., & Cao, Z. (2026) · Journal of Process Control · doi:10.1016/j.jprocont.2026.103797

Authors: Malossini, A., Blanzieri, E., & Ng, R. T.
Affiliations: University of Trento (IT)
University of British Columbia (CA)
Corresponding author: Malossini, Andrea
ORCID: Blanzieri, Enrico
Ng, Raymond T.
Venue: Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2006-06-09
Volume: 22
Issue: 17
Pages: 2114-2121
ISSN: 1367-4803 (ISSN-L)
1367-4811
DOI: 10.1093/bioinformatics/btl346
OpenAlex: W2170645625
Cited by: 80
References: 25
Citations by year: 2026: 1 · 2025: 4 · 2024: 1 · 2023: 4 · 2022: 4 · 2021: 6 · 2020: 9 · 2019: 4 · 2018: 1 · 2017: 1 · 2016: 10 · 2015: 4 · 2014: 8 · 2013: 7 · 2012: 3
FWCI: 1.36
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Bioinformatics (publishedVersion)
PubMed (publishedVersion)
Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: microarray data, data perturbation, classification stability, support vector machine, training data quality, label noise
MeSH terms: Artificial Intelligence, Computer Simulation, Data Interpretation, Statistical, Documentation, Models, Genetic, Pattern Recognition, Automated, Quality Control, Sensitivity and Specificity, Staining and Labeling, Reproducibility of Results, Information Storage and Retrieval, Artifacts, Sample Size, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Databases, Genetic

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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Detecting potential labeling errors in microarrays by data perturbation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks