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Reverse Engineering Regulatory Networks in Cells Using a Dynamic Bayesian Network and Mutual Information Scoring Function.

, , and . ICMLA, page 761-764. IEEE, (2017)

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Top-k Parametrized Boost., , , , , and . MIKE, volume 8891 of Lecture Notes in Computer Science, page 91-98. Springer, (2014)An empirical study of machine learning algorithms for cancer identification.. ICNSC, page 1-5. IEEE, (2018)Reverse Engineering Gene Regulatory Networks Using Sampling and Boosting Techniques., and . MLDM, volume 10358 of Lecture Notes in Computer Science, page 63-77. Springer, (2017)Reverse Engineering Regulatory Networks in Cells Using a Dynamic Bayesian Network and Mutual Information Scoring Function., , and . ICMLA, page 761-764. IEEE, (2017)A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction., , and . J. Bioinform. Comput. Biol., 16 (3): 1840014:1-1840014:31 (2018)GENEvaRX: A novel AI-driven method and web tool can identify critical genes and effective drugs for Lichen Planus., and . Eng. Appl. Artif. Intell., (2023)A link prediction approach to cancer drug sensitivity prediction., and . BMC Syst. Biol., 11 (S-5): 94:1-94:14 (2017)An Omnibus Test for Differential Distribution Analysis of Continuous Microbiome Data., , , and . IEEE Access, (2021)A Learning Framework to Improve Unsupervised Gene Network Inference., , and . MLDM, volume 9729 of Lecture Notes in Computer Science, page 28-42. Springer, (2016)Reverse Engineering Gene Regulatory Networks Using Graph Mining., , , and . MLDM (1), volume 10934 of Lecture Notes in Computer Science, page 335-349. Springer, (2018)