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  • 2020
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  • George Runger
2020

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
1 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

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

2018

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 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

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

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

4 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

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

5 Scopus citations
2017

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

6 Scopus citations
2016

Automated data mining methods for identifying energy efficiency opportunities using whole-building electricity data

Howard, P., Runger, G., Reddy, T. A. & Katipamula, S., 2016, ASHRAE Transactions - ASHRAE Winter Conference. Amer. Soc. Heating, Ref. Air-Conditoning Eng. Inc., Vol. 122. p. 422-433 12 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Discovering the Nature of Variation in Nonlinear Profile Data

Shi, Z., Apley, D. W. & Runger, G., Jul 2 2016, In : Technometrics. 58, 3, p. 371-382 12 p.

Research output: Contribution to journalArticle

5 Scopus citations

Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis

Howard, P., Apley, D. W. & Runger, G., Oct 26 2016, (Accepted/In press) In : IEEE Transactions on Neural Networks and Learning Systems.

Research output: Contribution to journalArticle

1 Scopus citations

EEG-based user performance prediction using random forest in a dynamic learning environment

Lujan-Moreno, G. A., Atkinson, R. & Runger, G., Jan 1 2016, Intelligent Tutoring Systems: Structure, Applications and Challenges. Nova Science Publishers, Inc., p. 105-128 24 p.

Research output: Chapter in Book/Report/Conference proceedingChapter

Monitoring temporal homogeneity in attributed network streams

Azarnoush, B., Paynabar, K., Bekki, J. & Runger, G., Jan 1 2016, In : Journal of Quality Technology. 48, 1, p. 28-43 16 p.

Research output: Contribution to journalArticle

31 Scopus citations

Multivariate bounded process adjustment schemes

Govind, N., del Castillo, E., Runger, G. & Janakiram, M., Jul 17 2016, (Accepted/In press) In : Quality Technology and Quantitative Management. p. 1-21 21 p.

Research output: Contribution to journalArticle

Predictive modeling using a nationally representative database to identify patients at risk of developing microalbuminuria

Villa-Zapata, L., Warholak, T., Slack, M., Malone, D., Murcko, A., Runger, G. & Levengood, M., Feb 1 2016, In : International Urology and Nephrology. 48, 2, p. 249-256 8 p.

Research output: Contribution to journalArticle

Time series representation and similarity based on local autopatterns

Baydogan, M. G. & Runger, G., Mar 1 2016, In : Data Mining and Knowledge Discovery. 30, 2, p. 476-509 34 p.

Research output: Contribution to journalArticle

39 Scopus citations