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'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns
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BACKGROUND: Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. RESULTS: We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. CONCLUSIONS: The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.
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“After normalization, gene shaving (22), bootstrapping (23), and cluster analysis were performed (24), and the slide effect, dye effect, variety effect, and duplicate design were taken into account in an ANOVA model (25).”
From A High-Fat Diet Coordinately Downregulates Genes Required for Mitochondrial Oxidative Phosphorylation in Skeletal Muscle (2005) · cites this paper as [22]
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Cites
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Self-citationGolub, 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
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
Self-citationAlon, 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
Tamayo, P., Slonim, D. K., Mesirov, J. P., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S., & Golub, T. R. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.6.2907 · Cited by 2,924
Distinctive gene expression patterns in human mammary epithelial cells and breast cancers
Perou, C. M., Jeffrey, S. S., van de Rijn, M., Rees, C. A., Eisen, M. B., Ross, D. T., Pergamenschikov, A., Williams, C. F., Zhu, S., Lee, J. C. F., et al. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.16.9212 · Cited by 1,402
Self-citationAn Information-Intensive Approach to the Molecular Pharmacology of Cancer
Weinstein, J. N., Myers, T. G., O'Connor, P. M., Friend, S. H., Fornace, A. J., Kohn, K. W., Fojo, T., Bates, S. E., Rubinstein, L., Anderson, N. L., et al. (1997) · Science · doi:10.1126/science.275.5298.343 · Cited by 1,183
Roth, F. P., Hughes, J. D., Estep, P. W., & Church, G. M. (1998) · Nature Biotechnology · doi:10.1038/nbt1098-939 · Cited by 939
Synexpression groups in eukaryotes
Niehrs, C., & Pollet, N. (1999) · Nature · doi:10.1038/990025 · Cited by 429
Research works printed but not linked: 2
- Tibshirani R, Hastie T, Eisen M, Ross D, Botstein D, Brown P: Clustering methods for the analysis of DNA microarray data. Technical report. Stanford: Department of Statistics, Stanford University; . 1999
- Hastie T, Tibshirani R, Eisen M, Brown P, Scherf U, Weinstein J, Alizadeh A, Staudt L, Botstein D: Gene shaving: a new class of clustering methods for expression arrays. Technical report. Stanford: Stanford University;. 2000
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Items 401–500 of 545
Zhu, D. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s1-s54 · Cited by 3
“[4], is that each gene can only belong to a single cluster.”
BackgroundMethodInfluentialHybrid SPR algorithm to select predictive genes for effectual cancer classification
Sundaram, A., VENKATA, N. L., & PARTHASARATHY, R. S. (2013) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-1203-138 · Cited by 3
“technology has allowed the simultaneous monitoring of thousands of genes, which accelerated the development of cancer classification using gene expression data [12–14].”
BackgroundA Clustering Method for Discovering Patterns Using Gene Regulatory Processes
Park, S., Choi, D., & Juni, C.-H. (2001) · doi:10.11234/gi1990.12.249 · Cited by 3
“We generate a simulation data that there exist 8 underlying patterns and random noises to compare our method with sliding window gene shaving [2] which modified gene shaving clustering algorithm [4].”
MethodStatistical Design and Data Analysis for Microarray Experiments
Goldstein, D., & Delorenzi, M. (2004) · CRC Press eBooks · doi:10.1201/9781420030921.ch1 · Cited by 3
Survey of Novel Feature Selection Methods for Cancer Classification
Okun, O. G. (2013) · doi:10.1002/9781118617151.ch16 · Cited by 3
Kontos, K., & Bontempi, G. (2009) · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · Cited by 3
Robust methods for locating multiple dense regions in complex datasets
Gupta, G. (2006) · Texas ScholarWorks (Texas Digital Library) · Cited by 3
de Brevern, A. G., Loirat, F., Badel, A., André, C., Vincens, P., & Hazout, S. (2002) · Computers & Chemistry · doi:10.1016/s0097-8485(02)00006-2 · Cited by 3
Tamayo, P., & Ramaswamy, S. (2003) · doi:10.1007/978-1-59259-386-6_6 · Cited by 3
Serban, N. (2008) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2007.12.016 · Cited by 2
“Hard clustering methods can also use regularization or filtering (see Ben-Dorr, Shamir and Yakhimi (1999), Hastie et al (2000) for regularization methods and Bar-Joseph, Gerber, Gifford and Jaakkola (2002); Serban and Wasserman (2005) for filtering methods).”
MethodA Hierarchical Mixture Model for Gene Expression Data
Scaccia, L., & Bartolucci, F. (2005) · Springer eBooks · doi:10.1007/3-540-27373-5_32 · Cited by 2
“A second generation of studies (see, for example, Brown et al. (2000) and Hastie et al. (2000)) sought further progress through more sophisticated and ad-hoc clustering strategies, employing resampling schemes, topology-constrained and/or supervised versions of partitioning algorithms, and "fuzzy"…”
BackgroundA gene expression analysis system for medical diagnosis
Maroulis, D., Iakovidis, D. K., Flaounas, I., & Karkanis, S. A. (2006) · Springer eBooks · doi:10.1007/0-387-34224-9_53 · Cited by 2
“[1] proposed the GeneClust software for microarray data analysis which implements hierarchical clustering and gene shaving algorithms [2].”
MethodClustering Methods for Gene-Expression Data
Flack, L. K. (2009) · IGI Global eBooks · doi:10.4018/978-1-60566-076-9.ch011 · Cited by 2
A Comparative Analysis of Rough Set Based Intelligent Techniques for Unsupervised Gene Selection
Banu, P. K. N., & Inbarani, H. H. (2013) · International Journal of System Dynamics Applications · doi:10.4018/ijsda.2013100103 · Cited by 2
Microarray data analysis: Gaining biological insights
Grewal, R. K., & Das, S. (2013) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2013.610124 · Cited by 2
“Principal component analysis (PCA) is another way of reducing dimensionality in the data.”
MethodInfluentialEnhanced Self-Organizing Map Neural Network for DNA Sequence Classification
Mohamed, M., Al-Mehdhar, A. A., Bamatraf, M., & Girgis, M. R. (2013) · Intelligent Information Management · doi:10.4236/iim.2013.51004 · Cited by 2
Network-based modular latent structure analysis
Yu, T., & Bai, Y. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s13-s6 · Cited by 2
“Clustering methods group co-expressed features together [8], which may help identify modules that are controlled by a single underlying signal [9,10].”
Wang, H., & Li, K. (2006) · Transactions of the Institute of Measurement and Control · doi:10.1191/0142331206tim175oa · Cited by 2
“In machine learning, there are two major approaches for dimensionality reduction, ie , the feature selection and the feature extraction (Eisen et al. , 1998; Furey et al. , 2000; Huang, 1996; Mao, 2004; West et al. , 2000).”
MethodLatent Process Decomposition Of High-Dimensional Count Data
Srivastava, S., & Doerge, R. W. (2005) · Cited by 2
Antibody Microarrays and Multiplexing
Zhou, J., Belov, L., Armstrong, N. J., & Christopherson, R. I. (2012) · Translational bioinformatics · doi:10.1007/978-94-007-5811-7_15 · Cited by 2
Principal component analysis (PCA) for clustering gene expression data
Yeung, K. Y., & Ruzzo, W. L. (2001) · Cited by 2
Wang, G., Lou, M., & Pananjady, A. (2024) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2024.3421618 · Cited by 2
“face recognition [28], [45], eigen-gene selection and shaving [1], [25], [29], handwriting classification [30], clustering of functional connectivity [26], and single-cell RNA sequencing analysis [53], to name but a few.”
BackgroundSystems Engineering Approaches To Gene Expression Profiling
Garg, S., & Achenie, L. E. K. (2002) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/s1570-7946(02)80175-4 · Cited by 2
Microarray-MD: A system for exploratory analysis of microarray gene expression data
Maroulis, D., Flaounas, I., Iakovidis, D. K., & Karkanis, S. A. (2006) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2006.06.008 · Cited by 2
“…2005 Received in revised form 30 May 2006 Accepted 8 June 2006 Keywords: Biomedical system a b s t r a c t In this paper, we present Microarray Medical Data explorer (Microarray-MD), a novel software system that is able to assist in the exploratory analysis of gene expression microarray data.”
MethodSelection-adjusted inference: an application to confidence intervals for cis-eQTL effect sizes
Panigrahi, S., Zhu, J., & Sabatti, C. (2018) · Europe PMC (PubMed Central) · doi:10.48550/arxiv.1801.08686 · Cited by 2
“Approach: a randomized conditional perspective Our methods build upon a conditional inference perspective that was introduced in Lee et al. (2016); Fithian et al.”
MethodSurvival Analysis with Gene Expression Arrays
Ankerst, D. P., Hardin, J., Faulkner, J. R., LeBlanc, M. L., & Crowley, J. J. (2003) · Handbook of statistics · doi:10.1016/s0169-7161(03)23037-6 · Cited by 2
Text Mining and Gene Expression Analysis Towards Combined Interpretation of High Throughput Data
Fundel, K. (2007) · doi:10.5282/edoc.7437 · Cited by 2
An Adaptive Strategy for Single- and Multi-Cluster Gene Assignment
Garg, S., Hansen, M. F., Rowe, D. W., & Achenie, L. E. K. (2003) · Biotechnology Progress · doi:10.1021/bp025648p · Cited by 2
“Data reduction techniques such as principal component analysis (PCA) ( 3 ), multidimensional scaling plots ( 4 ), hierarchical clustering ( 5 - 8 ), self-organizing maps ( 9 ), knowledge-based support vector machines ( 10 ), and “gene-shaving” ( 11 ) are a few of these techniques.”
MethodGene expression profiling in lymphoma diagnosis and research.
Chan, W. C. (2005) · PubMed · Cited by 2
Self-citationDesign and Analysis of Microarray Studies for Obesity Research
St-Onge, S. B. H.-M. M.-P. (2004) · CRC Press eBooks · doi:10.1201/9780203025925-9 · Cited by 2
Statistical Applications Using DNA Microarrays for Cancer Diagnosis and Prognosis
Matsui, S. (2005) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420027761.ch23 · Cited by 2
Statistical computation and inference for functional data analysis
Jiang, H. (2010) · SMARTech Repository (Georgia Institute of Technology) · Cited by 2
Computational Biology and Toxicogenomics
Kathleen Marchal, D., Engelen, K., Smet, F. D., & Moor, B. D. (2005) · CRC Press eBooks · doi:10.1201/9780849350351.ch3 · Cited by 2
Studying Evolution Using Genome Sequence Data
Lawrence, J. G. (2014) · ASM Press eBooks · doi:10.1128/9781555815622.ch2 · Cited by 2
Maitournam, A. (2015) · Journal of Applied Bioinformatics & Computational Biology · doi:10.4172/2329-9533.1000117 · Cited by 1
“The Cast model based on graph theory [49] uses as input the similarity matrix thus needs the same kind of adjustments like those used in the PCA or hierarchical clustering [50] used mixture of probability distribution functions as model of clustering analysis.”
MethodYuan, W., Li, Y., Han, Z., Chen, Y., Xie, J., Chen, J., Bi, Z., & Xi, J. (2024) · Biomedicines · doi:10.3390/biomedicines12092086 · Cited by 1
“This method is grounded in a simple yet profound principle: if a gene maintains consistent expression across a subset of samples, its expression level is considered conserved within that specific subset [33].”
MethodElastic net – based K2 algorithm for Bayesian network structure learning
Fallahnejad, M., Tabar, V. R., & Kazemi, M. (2025) · Statistics · doi:10.1080/02331888.2025.2591687 · Cited by 1
“Kalisch and Bühlmann [14] demonstrated the effectiveness of regularization in high-dimensional graphical models, while Hastie et al. [13] and later works [15,16] established the theoretical foundations and practical benefits of EN in variable selection and network reconstruction.”
Automated Hierarchical Density Shaving and Gene DIVER
Gupta, G., Liu, A. E., & Ghosh, J. (2006) · Cited by 1
Estimation of Distribution Algorithms in Gene Expression Data Analysis
Salehi, E., & Gras, R. (2012) · Intelligent systems reference library · doi:10.1007/978-3-642-23151-3_6 · Cited by 1
“A combination of Gene Shaving [63] and EDA or GA is used for this purpose.”
MethodCharacterization of Gene Expression Patterns for Classification of Breast Carcinomas
Andrulis, I. L., Gökgöz, N., & Bull, S. B. (2003) · doi:10.1007/978-1-59259-386-6_8 · Cited by 1
Brandl, M., Beck, D., Pham, T. D., Pham, T. D., Zhou, X., TANAKA, H. L., Oyama-Higa, M., Jiang, X., Sun, C., Kowalski, J., et al. (2011) · AIP conference proceedings · doi:10.1063/1.3596628 · Cited by 1
Reconstructing genetic regulation network: Problems and methods
Han, Z., Duan, D., Li, W., & Liu, H. (2009) · IEEE International Conference on Computer Science and Information Technology · doi:10.1109/iccsit.2009.5234743 · Cited by 1
“We classify all methods based on basic methodology.”
MethodIntegrated Analysis of Gene Expression and Gene Copy Number for Gene Shaving Based on ICA Approach
Wang, Y.-P. (2011) · International Conference on Bioinformatics and Biomedical Engineering · doi:10.1109/icbbe.2011.5779988 · Cited by 1
“The procedure of “gene shaving “ [1] can be regarded as”
BackgroundMethodMultimodality as a Criterion for Feature Selection in Unsupervised Analysis of Gene Expression Data
Li, Y., Sung, W.-K. K., & Miller, L. D. (2006) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2005.42 · Cited by 1
“Gene shaving [ 7 ] aims to find subsets of genes and samples for which there is large variance between samples, and small variance between genes.”
BackgroundPredictive model building for microarray data using generalized partial least squares model
Wu, B., & August, R. (2006) · Cited by 1
Hossain, A., & Khan, H. T. A. (2016) · Journal of Applied Statistics · doi:10.1080/02664763.2016.1142941 · Cited by 1
“proposed the popular gene shaving techniques using PCA to cluster highly variable and coherent genes inmicroarray datasets [4].”
MethodPlaid Model for Microarray Data: an Enhancement of the Pruning Step
Augugliaro, L., & Mineo, A. M. (2010) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-642-13312-1_47 · Cited by 1
“Several clustering methods have been developed in recent years in order to identify a bicluster, such as gene-shaving [3], EMMIX-GENE [6], EMMIX-WIRE [8], spectral biclustering [4] and the plaid model [5], among the others.”
MethodBose, S., Das, C., Chakraborty, A., & Chattopadhyay, S. (2013) · Lecture Notes on Information Theory · doi:10.12720/lnit.1.4.148-153 · Cited by 1
“Wilcoxon test [8] and Cox model score test [9] to”
BackgroundMethodSIFORM: shared informative factor models for integration of multi-platform bioinformatic data
An, X., Hu, J., & Do, K.-A. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw295 · Cited by 1
Clustering — Class Discovery in the Post-Genomic Era
Dopazo, J. (2007) · Springer eBooks · doi:10.1007/978-0-387-47509-7_6 · Cited by 1
“Later, specificity became a main concern as a natural reaction against quite liberal interpretations of microarray experiments made by some researchers, such as the fold change criterion to select differentially expressed genes.”
BackgroundDetecting the Most Insightful Parts of Documents Using a Regularized Attention-Based Model
Modarresi, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50420-5_20 · Cited by 1
Ou, H., Surendra, A., McDowell, G. S. V., Hashimoto-Roth, E., Xia, J., Bennett, S. A. L., & Čuperlović‐Culf, M. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae209 · Cited by 1
“…replacing with the feature maximum, (4) replacing with the feature median, (5) replacing with the feature mean, (6) using K-Nearest Neighbors (kNN) (Hastie et al. 2000, Troyanskaya et al. 2001), (7) using RF (Pantanowitz and Marwala 2009), or (8) using Multivariate Imputation by Chained Equations…”
Unsupervised Modified Adaptive Floating Search Feature Selection
Devakumari, D., & Thangavel, K. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-22714-1_37 · Cited by 1
Clustering Variables Based on Fuzzy Equivalence Relations
Adjenughwure, K., Botzoris, G., & Papadopoulos, B. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-19704-3_18 · Cited by 1
Chen, B., Zhai, W., & Kong, L. (2021) · AStA Advances in Statistical Analysis · doi:10.1007/s10182-021-00403-x · Cited by 1
“In linear regression, the “grouped variables” has been addressed many times in the literature, see Hastie et al. (2000); Zou and Hastie (2005) and references therein.”
BackgroundGene Expression Informatics and Analysis
van Kampen, A. H. C., Ruijter, J. M., van Schaik, B. D. C., Caron, H. N., & Versteeg, R. (2003) · doi:10.1002/0470867302.ch15 · Cited by 1
Clustering Functionally Similar Genes from Microarray Data
Maji, P., & Pal, S. K. (2012) · doi:10.1002/9781118119723.ch7 · Cited by 1
Rahnenführer, J., & Lengauer, T. (2007) · doi:10.1002/9783527619368.ch27 · Cited by 1
Efficient Clustering Methods for Tumor Classification With Microarrays
Rahnenführer, J. (2003) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-642-18991-3_76 · Cited by 1
Introduction to Statistical Methods for Complex Systems
Mary‐Huard, T., & Robin, S. S. (2011) · doi:10.1002/9781119970606.ch2 · Cited by 1
Software for Genetics/Genomics
Lu, Q. L., Song, Y. E., & Gray‐McGuire, C. (2014) · doi:10.1002/9781118596333.ch47 · Cited by 1
Analysis on protein structures using statistical and bioinformatical methods
Yan, A. (2008) · doi:10.31274/rtd-180813-17030 · Cited by 1
Gene Selection via a Spectral Approach
Wolf, L., Shashua, A., & Mukherjee, S. (2006) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvpr.2005.463 · Cited by 1
“A variety of algorithms exist for supervised gene selection: signalto-noise [9], recursive feature elimination [10], t-test m etrics [13], Wilcoxon rank sum test [13], and gene shaving [11].”
BackgroundMethodEmpirical Bayes Identication of Tumor Progression Genes from Microarray Data
Ghosh, D., & Chinnaiyan, A. M. (2007) · Biometrical Journal · doi:10.1002/bimj.200610312 · Cited by 1
“, 1998) and classification (Hastie et al., 2000; Dudoit et al., 2002a).”
MethodTrapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Cited by 1
Data analysis for microarray experiment and DNA barcode of life
Yu, C. (2008) · Rutgers University Community Repository (Rutgers University) · doi:10.7282/t38w3dpm · Cited by 1
Contributions to statistical techniques for the analysis of gene and protein expression data
Jung, K. (2006) · Technische Universität Dortmund Eldorado (Technische Universität Dortmund) · doi:10.17877/de290r-12679 · Cited by 1
“In order to see which genes have similar expression profiles over all replicates of a DNA microarray experiment one can group them by hierarchical clustering methods as proposed by Eisen et al. (1998) and Hastie et al. (2000).”
BackgroundUsing sparse CCA for vocabulary selection
Torres, D. A. (2009) · eScholarship (California Digital Library) · Cited by 1
Ling, M. H. (2009) · Minerva Access (University of Melbourne) · Cited by 1
Sparse principal component analysis and its $l_1$-relaxationPreprint
Dey, S. S., Mazumder, R., Molinaro, M., & Wang, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.00800 · Cited by 1
Bioinformatics in Functional Genomics
Kohane, I. S., & Butte, A. J. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat05444 · Cited by 1
Sparse regression and marginal testing using cluster prototypesPreprint
Reid, S., & Tibshirani, R. J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.00334 · Cited by 1
“Examples of the former include principal component regression, gene shaving of Hastie et al. (2000), tree harvesting of Hastie et al. (2001) and the canonical correlation clustering and subsequent sparse regression of Buhlmann et al. (2013).”
MethodSelf-citationOn the (lack of) robustness of gene expression data clustering
Gamalielsson, J., & Olsson, B. E. (2004) · Cited by 1
Design and Analysis of Microarray Studies for Obesity Research
Kim, K., DeLuca, M., Page, G. P., St‐Onge, M., Zhang, K., Allison, D. B., & Heymsfield, S. B. (2004) · Nutrition and disease prevention · doi:10.1201/9780203025925.ch7 · Cited by 1
Bioinformatics and Classification: The Analysis of Genome Expression Data
Lausen, B. (2002) · Studies in classification, data analysis, and knowledge organization · doi:10.1007/978-3-642-56181-8_50 · Cited by 1
SMSSVD: SubMatrix Selection Singular Value Decomposition
Henningsson, R., & Fontes, M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty566
“Developed specifically for Omics data and more particularly gene expression data such methods include the gene shaving method [6], tree harvesting [5], supervised principal components [1] and amplified marginal eigenvector regression [3].”
Fort, G., Lambert‐Lacroix, S., & Peyre, J. (2005) · French digital mathematics library (Numdam)
Statistical Genetics for Genomic Data Analysis
Lee, J. (2006) · Springer eBooks · doi:10.1007/978-1-84628-288-1_32
Detection of Phenotypes in Microarray Data Using Force- Directed Placement Transformss
Perez, D. V., & Robbins, K. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23199-5_24
“Independent row column (two-way) clustering is a standard approach for exploring correlations between the features (genes) and samples (patients) in microarray data [3, 4], although other clustering methods have been successfully applied [ 5-7 ].”
MethodControl of Type I Error Rates for Oncology Biomarker Discovery with High‐Throughput Platforms
Miecznikowski, J. C., Wang, D., & Liu, S. (2013) · Wiley eBooks · doi:10.1002/9783527665471.ch1
Bioinformatics in Functional Genomics
Kohane, I. S., & Butte, A. J. (2005) · Encyclopedia of Biostatistics · doi:10.1002/0470011815.b2a05099
Analyse von Biochips: Von der Sequenz zum System
Herwig, R., Schuchhardt, J., Chavez, L., & Lehrach, H. R. (2008) · Springer eBooks · doi:10.1007/978-3-540-69414-4_4
Genome wide functional annotation using mathematical programming
Garg, S., & Achenie, L. E. K. (2001) · Computer-aided chemical engineering/Computer aided chemical engineering · doi:10.1016/s1570-7946(01)80182-6
Personalised modelling framework and systems for Gene Data analysis and Biomedical applications
Hu, Y. (2010) · Tuwhera (Auckland University of Technology)
Unsupervised Data Mining Methods For Functional Data Analysis And Feature Selection
Rattakorn, P. (2009) · UTA ResearchCommons (University of Texas Arlington)
Bergmann, S. (2013) · Methods and principles in medicinal chemistry · doi:10.1002/9783527655984.ch12
Introduction and Overview of Technological Advances and Predictive Assays
Ramos, K. S., He, Q., & Gao, H. T. (2010) · Elsevier eBooks · doi:10.1016/b978-0-08-046884-6.00235-9
Chang, J., Zhou, W., & Zhou, W. (2015) · arXiv (Cornell University)
Dragomir, A. (2006) · doi:10.12681/eadd/26361
“, 2001), gene shaving (Hastie et al., 2000) or the dynamically extended SOM [sNetSOM] (Mavroudi et al, 2002).”
BackgroundEntwicklung und Vergleich biostatistischer Methoden zur Auswertung von Microarray Experimenten
Clevert, D.-A. (2012) · Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver · doi:10.17169/refubium-16227
Gene expression signature discovery with independent component analysis
Szu, H. H., & Portnoy, D. (2003) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.501628
“x → i = A [ ] s → i, (3) where x → is a column vector of expression values, and s → is a column vector of signal values.”
BackgroundA hybrid unsupervised and supervised clustering applied to microarray data
Malutan, R., Gómez‐Vilda, P., & Borda, M. (2013) · International Journal of Advances in Telecommunications Electrotechnics Signals and Systems · doi:10.11601/ijates.v2i3.21
Giurcăneanu, C. D., Mircean, C., Fuller, G. N., & Tăbuş, I. (2007) · Springer eBooks · doi:10.1007/0-387-26288-1_7
“The name of the original algorithm is “Gene Shaving” (GS) ( Hastie et al., 2000a ), and we present it in a separate section.”
BackgroundMethodInfluentialIdentifying disease genes from gene expression data based on singular value decomposition
Zhang, H., Song, X., & Zhang, X. (2011) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2011.6098516
Combined Clustering Methods for Microarray Data Analysis
Malutan, R., Gómez‐Vilda, P., & Borda, M. (2013) · Advanced engineering forum · doi:10.4028/www.scientific.net/aef.8-9.508
“According to [2], the algorithm consists of the following steps: 1.”
MethodClustering Microarray Data: Theoretical and Practical Issues
Lascio, F. M. L. D., & Giannerini, S. (2012) · Communication in Statistics- Theory and Methods · doi:10.1080/03610926.2011.654036
“They noted that these kind of methods have been used for different purposes like the identification of co-regulated to Hastie et al. (2000).”
MethodA Bayesian Framework for Statistical Inference from Gene Expression Data
Sreekumar, J., & Jose, K. K. (2007) · American Journal of Mathematical and Management Sciences · doi:10.1080/01966324.2007.10737697
MMSE: A generalized coherence measure for identifying linear patterns
Chen, S., Liu, J., & Zeng, T. (2014) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2014.6999206
“Various techniques have been proposed to identify nonlinear coherent bicluster patterns from gene expression data: (1) two-way hierarchical clustering method (HCL) [3]; (2) the Bayesian-based biclustering (BBC) [4] that are based on a rigorous statical model; (3) the qualitative biclustering algorithm (QUBIC) that can…”
MethodOn the Use of Multidimensional Data Analysis Techniques for Corporate Valuation
Vintilă, G., & Gherghina, Ş. C. (2014) · Modern Applied Science · doi:10.5539/mas.v8n3p202
Metadata
- Authors
- Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., & Brown, P.
- Affiliations
- Stanford University (US)
- Lawrence Berkeley National Laboratory (US)
- Stanford Medicine (US)
- National Institutes of Health (US)
- University of Nebraska Medical Center (US)
- Corresponding author
- Tibshirani, Robert
- ORCID
- Venue
- Genome Biology
- Published
- 2000-08-04
- Volume
- 1
- Issue
- 2
- Pages
- RESEARCH0003-RESEARCH0003
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC15015
- OpenAlex
- W1774711529
- Cited by
- 547
- References
- 18
- Citations by year
- 2025: 1 · 2024: 8 · 2022: 7 · 2021: 7 · 2020: 5 · 2019: 11 · 2018: 12 · 2017: 11 · 2016: 17 · 2015: 20 · 2014: 20 · 2013: 21 · 2012: 18
- FWCI
- 11.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Genome biology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Lincoln (University of Nebraska) (submittedVersion)
- Funders
- National Science Foundation
- National Institutes of Health
- National Cancer Institute (U01CA084967, P01CA034233, CA 34233, UOI-CA 84967)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- gene expression clustering, gene expression analysis, supervised clustering, hierarchical clustering, diffuse large B-cell lymphoma, survival prediction, gene expression signatures, microarray data
- MeSH terms
- Algorithms, Humans, RNA, Messenger, Gene Expression Regulation, Neoplastic, Cluster Analysis, Survival Analysis, Lymphoma, B-Cell, Lymphoma, Large B-Cell, Diffuse, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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