Optimization of Classifier Ensemble Diversity

SSRN Electronic Journal · 2024-01-01

Optimization of classifier ensemble diversity

Academia molecular biology and genomics. · 2024-07-30

Ensemble diversity was investigated for 12 classifiers and 16 datasets using a generalization error and ambiguity decomposition of the model bias-variance relationship. Diversity was optimized using a genetic algorithm and particle swarm optimization. Classifiers with the greatest contribution to ensemble diversity were learning vector quantization, naïve Bayes classifier, and supervised neural gas, whereas the supervised artificial neural network (SANN) and support vector machines (SVM) classifiers were among the least informative for diversity. An important observation was that classifiers with the greatest optimized employment weights in diverse ensembles also had the greatest association between individual unoptimized diversity, D, and number of classes among datasets, with little association with dataset number of objects or features. The SANN and SVM classifiers had a significant association with the number of objects in datasets and were employed infrequently in diverse ensembles. In conclusion, the diversity of a classifier is more dependent on the number of classes of datasets and less dependent on the number of objects or features. A greater ensemble employment weight for a classifier also occurs when its range of generalization error and ambiguity over all datasets overlap.

Supplementary table 6 from Nuclear Receptor Corepressor 1 Expression and Output Declines with Prostate Cancer Progression

2023-03-31

<p>NCOR1 signature</p>

Supplementary Table 2 from Nuclear Receptor Corepressor 1 Expression and Output Declines with Prostate Cancer Progression

2023-03-31

<p>DHT regulated pathways</p>

Supplementary Table 3 from Nuclear Receptor Corepressor 1 Expression and Output Declines with Prostate Cancer Progression

2023-03-31

<p>AR signature in LNCaP cells</p>

Supplementary Table 1 from Nuclear Receptor Corepressor 1 Expression and Output Declines with Prostate Cancer Progression

2023-03-31

<p>NCOR1 regulated pathways</p>

Supplementary Figures from Nuclear Receptor Corepressor 1 Expression and Output Declines with Prostate Cancer Progression

2023-03-31

<p>Supplementary Figure 1 Comparison of NCOR1 and NCOR2 knockdown in LAPC4 and LNCaP cells Supplementary Figure 2 NCOR1 regulates motility of LNCaP cells Supplementary Figure 3 AR signature is present among bicalutamide treated genes in both control and NCOR1 depleted cells Supplementary Figure 4 Comparative levels of UGT2B15 and UGT2B17 mRNA expression in LNCaP and C4-2 cells Supplementary Figure 5 Loss of both UGT2B 15 and UGT2B 17 lead to elevated DHT-dependent PSA expression. Supplementary Figure 6 NCOR1 is required for optimal TARP and CHEK1 expression in LAPC4 cells Supplementary Figure 7 AR is recruited to PSA enhancer and INPP4B promoter after NCOR1 depletion Supplementary Figure 8 Examples of variable NCOR1 staining in normal prostate epithelium, PIN, and prostate cancer on tissue microarray.</p>

Supplementary Table 4 from Nuclear Receptor Corepressor 1 Expression and Output Declines with Prostate Cancer Progression

2023-03-31

<p>SIAH2 signature in LNCaP cells</p>

Data from Nuclear Receptor Corepressor 1 Expression and Output Declines with Prostate Cancer Progression

2023-03-31

<div>Abstract<p><b>Purpose:</b> Castration therapy in advanced prostate cancer eventually fails and leads to the development of castration-resistant prostate cancer (CRPC), which has no cure. Characteristic features of CRPC can be increased androgen receptor (AR) expression and altered transcriptional output. We investigated the expression of nuclear receptor corepressor 1 (NCOR1) in human prostate and prostate cancer and the role of NCOR1 in response to antiandrogens.</p><p><b>Experimental Design:</b> NCOR1 protein levels were compared between matched normal prostate and prostate cancer in 409 patient samples. NCOR1 knockdown was used to investigate its effect on bicalutamide response in androgen-dependent prostate cancer cell lines and transcriptional changes associated with the loss of NCOR1. NCOR1 transcriptional signature was also examined in prostate cancer gene expression datasets.</p><p><b>Results:</b> NCOR1 protein was detected in cytoplasm and nuclei of secretory epithelial cells in normal prostate. Both cytoplasmic and nuclear NCOR1 protein levels were lower in prostate cancer than in normal prostate. Prostate cancer metastases show significant decrease in NCOR1 transcriptional output. Inhibition of LNCaP cellular proliferation by bicalutamide requires NCOR1. NCOR1-regulated genes suppress cellular proliferation and mediate bicalutamide resistance. In the mouse, NCOR1 is required for bicalutamide-dependent regulation of a subset of the AR target genes.</p><p><b>Conclusions:</b> In summary, we demonstrated that NCOR1 function declines with prostate cancer progression. Reduction in NCOR1 levels causes bicalutamide resistance in LNCaP cells and compromises response to bicalutamide in mouse prostate <i>in vivo</i>. <i>Clin Cancer Res; 22(15); 3937–49. ©2016 AACR</i>.</p></div>

Supplementary Figures from Nuclear Receptor Corepressor 1 Expression and Output Declines with Prostate Cancer Progression

2023-03-31

<p>Supplementary Figure 1 Comparison of NCOR1 and NCOR2 knockdown in LAPC4 and LNCaP cells Supplementary Figure 2 NCOR1 regulates motility of LNCaP cells Supplementary Figure 3 AR signature is present among bicalutamide treated genes in both control and NCOR1 depleted cells Supplementary Figure 4 Comparative levels of UGT2B15 and UGT2B17 mRNA expression in LNCaP and C4-2 cells Supplementary Figure 5 Loss of both UGT2B 15 and UGT2B 17 lead to elevated DHT-dependent PSA expression. Supplementary Figure 6 NCOR1 is required for optimal TARP and CHEK1 expression in LAPC4 cells Supplementary Figure 7 AR is recruited to PSA enhancer and INPP4B promoter after NCOR1 depletion Supplementary Figure 8 Examples of variable NCOR1 staining in normal prostate epithelium, PIN, and prostate cancer on tissue microarray.</p>