Search Results - Liu, Zhifa

A powerful association for comorbidity analysis based on score based test by Liu, Zhifa

Get full text Get full text

Feature selection and prediction with a Markov blanket structure learning algorithm by Tan, Yuan, Liu, Zhifa

Get full text Get full text

An R package that automatically collects and archives details for reproducible computing by Liu, Zhifa, Pounds, Stan

Get full text Get full text Get full text

Empirical evaluation of scoring functions for Bayesian network model selection by Liu, Zhifa, Malone, Brandon, Yuan, Changhe

Get full text Get full text Get full text

Machine Learning Analysis of the Relationship Between Changes in Immunological Parameters and Changes in Resistance to Listeria monocytogenes: A New Approach for Risk Assessment an... by Liu, Zhifa, Yuan, Changhe, Pruett, Stephen B.

Get full text Get full text Get full text

Genetic Association Test for Multiple Traits at Gene Level by Guo, Xiaobo, Liu, Zhifa, Wang, Xueqin, Zhang, Heping

Get full text Get full text Get full text

Large Scale Association Analysis for Drug Addiction: Results from SNP to Gene by Guo, Xiaobo, Liu, Zhifa, Wang, Xueqin, Zhang, Heping

Get full text Get full text Get full text

NCK2 Is Significantly Associated with Opiates Addiction in African-Origin Men by Liu, Zhifa, Guo, Xiaobo, Jiang, Yuan, Zhang, Heping

Get full text Get full text Get full text

Our strategy to achieve and document reproducible computing by Enyinda, Nisrine, Liu, Zhifa, Negatu, Areg, Pounds, Stan

Get full text Get full text

A genomic random interval model for statistical analysis of genomic lesion data by Pounds, Stan, Cheng, Cheng, Li, Shaoyu, Liu, Zhifa, Zhang, Jinghui, Mullighan, Charles

Get full text Get full text Get full text

The most informative spacing test effectively discovers biologically relevant outliers or multiple modes in expression by Pawlikowska, Iwona, Wu, Gang, Edmonson, Michael, Liu, Zhifa, Gruber, Tanja, Zhang, Jinghui, Pounds, Stan

Get full text Get full text Get full text

Prognostic Significance of Major Histocompatibility Complex Class II Expression in Pediatric Adrenocortical Tumors: A St. Jude and Children's Oncology Group Study by Pinto, Emilia Modolo, Rodriguez-Galindo, Carlos, Choi, John Kim, Pounds, Stanley, Liu, Zhifa, Neale, Geoff, Finkelstein, David, Hicks, John, Pappo, Alberto S., Figueiredo, Bonald, Ribeiro, Raul C., Zambetti, Gerard P.

Get full text Get full text Get full text

SVSI: Fast and Powerful Set-Valued System Identification Approach to Identifying Rare Variants in Sequencing Studies for Ordered Categorical Traits by Bi, Wenjian, Kang, Guolian, Zhao, Yanlong, Cui, Yuehua, Yan, Song, Li, Yun, Cheng, Cheng, Pounds, Stanley B., Borowitz, Michael J., Relling, Mary V., Yang, Jun J., Liu, Zhifa, Pui, Ching-Hon, Hunger, Stephen P., Hartford, Christine M, Leung, Wing, Zhang, Ji-Feng

Get full text Get full text Get full text

Genomic landscape of pediatric adrenocortical tumors by Pinto, Emilia M., Chen, Xiang, Easton, John, Finkelstein, David, Liu, Zhifa, Pounds, Stanley, Rodriguez-Galindo, Carlos, Lund, Troy C., Mardis, Elaine R., Wilson, Richard K., Boggs, Kristy, Yergeau, Donald, Cheng, Jinjun, Mulder, Heather L., Manne, Jayanthi, Jenkins, Jesse, Mastellaro, Maria J., Figueiredo, Bonald C., Dyer, Michael A., Pappo, Alberto, Zhang, Jinghui, Downing, James R., Ribeiro, Raul C., Zambetti, Gerard P.

Get full text Get full text Get full text

The Genomic Landscape of Childhood and Adolescent Melanoma by Lu, Charles, Zhang, Jinghui, Nagahawatte, Panduka, Easton, John, Lee, Seungjae, Liu, Zhifa, Ding, Li, Wyczalkowski, Matthew A, Valentine, Marcus, Navid, Fariba, Mulder, Heather, Tatevossian, Ruth G, Dalton, James, Davenport, James, Yin, Zhirong, Edmonson, Michael, Rusch, Michael, Wu, Gang, Li, Yongjin, Parker, Matthew, Hedlund, Erin, Shurtleff, Sheila, Raimondi, Susana, Bhavin, Vadodaria, Donald, Yergeau, Mardis, Elaine R, Wilson, Richard K, Evans, William E, Ellison, David W, Pounds, Stanley, Dyer, Michael, Downing, James R, Pappo, Alberto, Bahrami, Armita

Get full text Get full text Get full text