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Global functional profiling of gene expression☆☆This work was funded in part by a Sun Microsystems grant awarded to S.D., NIH Grant HD36512 to S.A.K., a Wayne State University SOM Dean’s Post-Doctoral Fellowship, and an NICHD Contraception and Infertility Loan to G.C.O. Support from the WSU MCBI mode is gratefully appreciated.

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Transcriptome analysis reveals link between proteasomal and mitochondrial pathways in Parkinson’s disease

Duke, D. C., Moran, L. B., Kalaitzakis, M. E., Deprez, M., Dexter, D. T., Pearce, R. K. B., & Graeber, M. B. (2006) · Neurogenetics · doi:10.1007/s10048-006-0033-5 · Cited by 93 · see citing works

It's in your blood: spectral biomarker candidates for urinary bladder cancer from automated FTIR spectroscopy

Ollesch, J., Heinze, M., Heise, H. M., Behrens, T., Brüning, T., & Gerwert, K. B. (2014) · Journal of Biophotonics · doi:10.1002/jbio.201300163 · Cited by 92 · see citing works

Elucidating endometrial function in the post-genomic era

Giudice, L. C. (2003) · Human Reproduction Update · doi:10.1093/humupd/dmg019 · Cited by 92 · see citing works

Molecular classification of cancer: unsupervised self-organizing map analysis of gene expression microarray data.

Covell, D. G., Wallqvist, A., Rabow, A. A., & Thanki, N. (2003) · PubMed · Cited by 87 · see citing works

Selection for Chromosome Architecture in Bacteria

Hendrickson, H. L., & Lawrence, J. G. (2006) · Journal of Molecular Evolution · doi:10.1007/s00239-005-0192-2 · Cited by 86 · see citing works

Dimension reduction strategies for analyzing global gene expression data with a response

Chiaromonte, F., & Martinelli, J. (2002) · Mathematical Biosciences · doi:10.1016/s0025-5564(01)00106-7 · Cited by 82 · see citing works

A new paradigm for profiling testicular gene expression during normal and disturbed human spermatogenesis

Feig, C., Kirchhoff, C., Ivell, R., Naether, O., Schulze, W., & Spiess, A. (2006) · Molecular Human Reproduction · doi:10.1093/molehr/gal097 · Cited by 81 · see citing works

Variable selection and pattern recognition with gene expression data generated by the microarray technology

Szabó, A., Boucher, K. M., Carroll, W. L., Klebanov, L. B., Tsodikov, A. D., & Yakovlev, A. Y. (2002) · Mathematical Biosciences · doi:10.1016/s0025-5564(01)00103-1 · Cited by 81 · see citing works

Molecular indices of viral disease development in wild migrating salmon†

Miller, K. M., Günther, O. P., Li, S., Kaukinen, K. H., & Ming, T. J. (2017) · Conservation Physiology · doi:10.1093/conphys/cox036 · Cited by 80 · see citing works

MOGSA: Integrative Single Sample Gene-set Analysis of Multiple Omics Data

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Applications of Bayesian network models in predicting types of hematological malignancies

Agrahari, R., Foroushani, A. B. K., Docking, R., Chang, L., Duns, G., Hudoba, M., Karsan, A., & Zare, H. (2018) · Scientific Reports · doi:10.1038/s41598-018-24758-5 · Cited by 76 · see citing works

Overview of Commonly Used Bioinformatics Methods and Their Applications

Kapetanović, I. M., Rosenfeld, S., & Izmirlian, G. (2004) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1310.003 · Cited by 76 · see citing works

The liver pharmacological and xenobiotic gene response repertoire

Natsoulis, G., Pearson, C. I., Gollub, J., Eynon, B. P., Ferng, J., Nair, R. V., Idury, R., Lee, M. D., Fielden, M. R., Brennan, R., et al. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.9 · Cited by 77 · see citing works

New modifications and applications of fuzzy -means methodology

Berget, I., Mevik, B., & Næs, T. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.10.020 · Cited by 76 · see citing works

Bayesian ensemble methods for survival prediction in gene expression data

Bonato, V., Baladandayuthapani, V., Broom, B. M., Sulman, E. P., Aldape, K., & Do, K.-A. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq660 · Cited by 74 · see citing works

Development and validation of biomarker classifiers for treatment selection

Simon, R. M. (2007) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2007.06.010 · Cited by 73 · see citing works

Permutation-validated principal components analysis of microarray data

Landgrebe, J., Wurst, W., & Welzl, G. (2002) · Genome biology · doi:10.1186/gb-2002-3-4-research0019 · Cited by 73 · see citing works

Comment Review Reports Deposited Research Interactions Information Refereed

Shaving', R. G., Hastie, T. J., Tibshirani, R. J., Eisen, M. B., Alizadeh, A. A., Levy, R., Staudt, L. M., Chan, W. C., Botstein, D., & Brown, P. O. (2000)

GeneClust

Do, K.-A., Broom, B. M., & Wen, S. (2003) · Statistics for biology and health · doi:10.1007/0-387-21679-0_15 · Cited by 5 · see citing works

Application of Gene Shaving and Mixture Models to Cluster Microarray Gene Expression Data

Do, K.-A., McLachlan, G. J., Bean, R. W., & Wen, S. (2007) · Cancer Informatics · doi:10.1177/117693510700500002 · Cited by 4 · see citing works

SYSTEMS BIOLOGY SPECIAL ISSUE Application of Gene Shaving and Mixture Models to Cluster Microarray Gene

Data, E., Do, K.-A., McLachlan, G. J., Bean, R. W., & Wen, S. (2012)

Bagged gene shaving for the robust clustering of high-throughput data

Broom, B. M., Sulman, E. P., Do, K., Edgerton, M. E., & Aldape, K. (2010) · International Journal of Bioinformatics Research and Applications · doi:10.1504/ijbra.2010.035997 · Cited by 6 · see citing works