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Comparison of Non-negative Matrix Factorization Methods for Clustering Genomic Data.

M. Hou, Y. Gao, J. Liu, J. Shang, and C. Zheng. ICIC (2), volume 9772 of Lecture Notes in Computer Science, page 290-299. Springer, (2016)

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Performance analysis of novel methods for detecting epistasis.J. Shang, J. Zhang, Y. Sun, D. Liu, D. Ye, and Y. Yin. BMC Bioinformatics, (2011)An Improved Ant Colony Optimization Algorithm for the Detection of SNP-SNP Interactions.Y. Sun, J. Shang, J. Liu, and S. Li. ICIC (3), volume 9773 of Lecture Notes in Computer Science, page 21-32. Springer, (2016)NIPMI: A Network Method Based on Interaction Part Mutual Information to Detect Characteristic Genes From Integrated Data on Multi-Cancers.Q. Ding, J. Shang, Y. Sun, G. Liu, F. Li, X. Yuan, and J. Liu. IEEE Access, (2019)epiACO - a method for identifying epistasis based on ant Colony optimization algorithm.Y. Sun, J. Shang, J. Liu, S. Li, and C. Zheng. BioData Min., 10 (1): 23:1-23:17 (2017)EpiMiner: A three-stage co-information based method for detecting and visualizing epistatic interactions.J. Shang, J. Zhang, Y. Sun, and Y. Zhang. Digital Signal Processing, (2014)A Review of Ant Colony Optimization Based Methods for Detecting Epistatic Interactions.J. Shang, X. Wang, X. Wu, Y. Sun, Q. Ding, J. Liu, and H. Zhang. IEEE Access, (2019)Network analysis based on low-rank method for mining information on integrated data of multi-cancers.M. Hou, Y. Gao, J. Liu, L. Dai, X. Kong, and J. Shang. Computational Biology and Chemistry, (2019)HC-HDSD: A method of hypergraph construction and high-density subgraph detection for inferring high-order epistatic interactions.Q. Ding, J. Shang, Y. Sun, X. Wang, and J. Liu. Computational Biology and Chemistry, (2019)L21-iPaD: An efficient method for drug-pathway association pairs inference.D. Wang, C. Zheng, Y. Gao, J. Liu, S. Wu, and J. Shang. BIBM, page 664-669. IEEE, (2016)Comparison of Non-negative Matrix Factorization Methods for Clustering Genomic Data.M. Hou, Y. Gao, J. Liu, J. Shang, and C. Zheng. ICIC (2), volume 9772 of Lecture Notes in Computer Science, page 290-299. Springer, (2016)

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Comparison of Non-negative Matrix Factorization Methods for Clustering Genomic Data.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Comparison of Non-negative Matrix Factorization Methods for Clustering Genomic Data.

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Comparison of Non-negative Matrix Factorization Methods for Clustering Genomic Data.