Matics (2017) 18:Table 1 Summary of some characteristics of VIMsPVIM (CART-RF)  Accuracy after
Matics (2017) 18:Table 1 Summary of some characteristics of VIMsPVIM (CART-RF)  Accuracy after variable permutation CART-RF Univariate randomForest [18, 19] randomForestSRC [20?2] Yes Not defined Not defined party [23?5] party [23?5] Univariate Univariate CIT-RF CIT-RF CART-RF Categorical varSelRF [27, 28] Accuracy after variable permutation Alternative of PVIM; Conditional permutation Backward elimination [13, 15]   PVIM (CIT-RF) CPVIM varSelRF varSelMD [10, 33] Variable selection based on MD CART-RF All (multivariate included) randomForestSRC [20?2] No No IPM  and this manuscript Variables intervening in prediction CART-RF or CIT-RF All (multivariate included) Additional fileMethodsGVIMMain
Yang et al. BMC Bioinformatics (2017) 18:481 DOI 10.1186/s12859-017-1926-zRESEARCH ARTICLEOpen AccessAnalysis of breast cancer subtypes by AP-ISA biclusteringLiying Yang1*, Yunyan Shen1, Xiguo Yuan1, Junying Zhang1 and Jianhua Wei2*AbstractBackground: Gene expression profiling has led to the definition of breast cancer molecular subtypes: Basal-like, HER2-enriched, LuminalA, LuminalB and Normal-like. Different subtypes exhibit diverse responses to treatment. In the past years, several traditional clustering algorithms have been applied to analyze gene Sodium dichloroacetate expression profiling. However, accurate identification of breast cancer subtypes, especially within highly variable LuminalA subtype, remains a challenge. Furthermore, the relationship between DNA methylation and expression level PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/26163092 in different breast cancer subtypes is not clear. Results: In this study, a modified ISA biclustering algorithm, termed AP-ISA, was proposed to identify breast cancer subtypes. Comparing with ISA, AP-ISA provides PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28578125 the optimized strategy to select seeds and thresholds in the circumstance that prior knowledge is absent. Experimental results on 574 breast cancer samples were evaluated using clinical ER/PR information, PAM50 subtypes and the results of five peer to peer methods. One remarkable point in the experiment is that, AP-ISA divided the expression profiles of the luminal samples into four distinct classes. Enrichment analysis and methylation analysis showed obvious distinction among the four subgroups. Tumor variability within the Luminal subtype is observed in the experiments, which could contribute to the development of novel directed therapies. Conclusions: Aiming at breast cancer subtype classification, a novel biclustering algorithm AP-ISA is proposed in this paper. AP-ISA classifies breast cancer into seven subtypes and we argue that there are four subtypes in luminal samples. Comparison with other methods validates the effectiveness of AP-ISA. New genes that would be useful for targeted treatment of breast cancer were also obtained in this study. Keywords: Breast cancer, Subtype, Classification, Biclustering, Gene expression profiles, MethylationBackground Breast cancer is a complex and heterogeneous disease and one of the leading causes of cancer-related death among women. The prognosis of breast cancer patients has been improved over time. However, further improvements in targeted treatment for breast cancer patients are expecting to solve the problem that why current therapy has effect only on a portion of the patients. A major milestone on the way to this goal is the definition of breast cancer molecular subtypes based on gene expression profiles: Basal-like , LuminalA, LuminalB,* Correspondence: firstname.lastname@example.org; email@example.com 1 Sch.
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