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  • George Runger

A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas

Lin, S., Wang, C., Zarei, S., Bell, D. A., Kerr, S. E., Runger, G. & Kocher, J. P. A., Nov 27 2018, In : BMC Genomics. 19, 1, 841.

Research output: Contribution to journalArticle

Open Access
1 Scopus citations

Correction: Performance of next-generation sequencing on small tumor specimens and/or low tumor content samples using a commercially available platform(PLoS ONE (2018) 13:4 (e0196556) DOI: 10.1371/journal.pone.0196556)

Morris, S. M., Subramanian, J., Gel, E., Runger, G., Thompson, E. J., Mallery, D. W. & Weiss, G. J., Jun 1 2018, In : PLoS One. 13, 6, e0200224.

Research output: Contribution to journalComment/debate

CRAFTER: a Tree-ensemble Clustering Algorithm for Static Datasets with Mixed Attributes and High Dimensionality

Lin, S., Azarnoush, B. & Runger, G., Feb 16 2018, (Accepted/In press) In : IEEE Transactions on Knowledge and Data Engineering.

Research output: Contribution to journalArticle

3 Scopus citations

Dynamic incorporation of prior knowledge from multiple domains in biomarker discovery

Guan, X., Runger, G. & Liu, L., Mar 11 2020, In : BMC bioinformatics. 21, 77.

Research output: Contribution to journalArticle

Open Access

GCRNN: Group-Constrained Convolutional Recurrent Neural Network

Lin, S. & Runger, G., Dec 7 2017, (Accepted/In press) In : IEEE Transactions on Neural Networks and Learning Systems.

Research output: Contribution to journalArticle

5 Scopus citations

Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data

Shi, Z., Apley, D. W. & Runger, G., Jan 1 2019, In : Journal of Quality Technology. 51, 1, p. 3-20 18 p.

Research output: Contribution to journalArticle

Identifying nonlinear variation patterns with deep autoencoders

Howard, P., Apley, D. W. & Runger, G., Dec 2 2018, In : IISE Transactions. 50, 12, p. 1089-1103 15 p.

Research output: Contribution to journalArticle

Interpretable regularized class association rules algorithm for classification in a categorical data space

Azmi, M., Runger, G. C. & Berrado, A., May 2019, In : Information Sciences. 483, p. 313-331 19 p.

Research output: Contribution to journalArticle

5 Scopus citations

Matched Forest: Supervised learning for high-dimensional matched case-control studies

Shomal Zadeh, N., Lin, S., Runger, G. C. & Wren, J., Mar 1 2020, In : Bioinformatics. 36, 5, p. 1570-1576 7 p.

Research output: Contribution to journalArticle

Performance of next-generation sequencing on small tumor specimens and/or low tumor content samples using a commercially available platform

Morris, S., Subramanian, J., Gel, E., Runger, G., Thompson, E., Mallery, D. & Weiss, G., Apr 2018, In : PloS one. 13, 4, e0196556.

Research output: Contribution to journalArticle

3 Scopus citations

Projection-free kernel principal component analysis for denoising

Bui, A. T., Im, J. K., Apley, D. W. & Runger, G., Sep 10 2019, In : Neurocomputing. 357, p. 163-176 14 p.

Research output: Contribution to journalArticle

1 Scopus citations

Query-by-committee improvement with diversity and density in batch active learning

Kee, S., del Castillo, E. & Runger, G., Jul 2018, In : Information Sciences. 454-455, p. 401-418 18 p.

Research output: Contribution to journalArticle

7 Scopus citations

Rejoinder on: “On active learning methods for manifold data”

Li, H., Del Castillo, E. & Runger, G., Mar 1 2020, In : Test. 29, 1, p. 42-49 8 p.

Research output: Contribution to journalComment/debate

Whole blood FPR1 mRNA expression predicts both non-small cell and small cell lung cancer

Morris, S., Vachani, A., Pass, H. I., Rom, W. N., Ryden, K., Weiss, G. J., Hogarth, D. K., Runger, G., Richards, D., Shelton, T. & Mallery, D. W., Jun 1 2018, In : International Journal of Cancer. 142, 11, p. 2355-2362 8 p.

Research output: Contribution to journalArticle

4 Scopus citations