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LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer

机译：Logloss-Beraf：基于集合的机器学习模型，用于构建高精度诊断甲基化位点，占前列腺癌的异质性

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K. Babalyan; R. Sultanov; E. Generozov; E. Sharova; E. Kostryukova; A. Larin; A. Kanygina; V. Govorun; G. Arapidi;
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1. Transition zone prostate cancer: Logistic regression and machine‐learning models of quantitative ADC, shape and texture features are highly accurate for diagnosis [J] . Wu Mark, Krishna Satheesh, Thornhill Rebecca E., Journal of magnetic resonance imaging: JMRI . 2019,第3期

机译：过渡带前列腺癌：定量ADC的逻辑回归和机器学习模型，形状和纹理特征非常准确地进行诊断
2. Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data [J] . Nature protocols erecipes for researchers . 2020,第2期

机译：机器学习工作流程以估算DNA甲基化微阵列数据精密癌症诊断的阶级概率
3. Tumor Ensemble-Based Modeling and Visualization of Emergent Angiogenic Heterogeneity in Breast Cancer [J] . Spyros K. Stamatelos, Akanksha Bhargava, Eugene Kim, Scientific reports. . 2019,第1期

机译：基于肿瘤的肿瘤血管癌血管生成异质性的肿瘤型建模与可视化
4. Protein-Protein Interaction Network Constructing Based on Text Mining and Reinforcement Learning with Application to Prostate Cancer [C] . Zhu Fei, Liu Quan, Zhang Xiaofang, IEEE International Conference on Trust, Security and Privacy in Computing and Communications;IEEE International Conference on Big Data Science and Engineering;IEEE International Symposium on Parallel and Distributed Processing with Applications . 2015

机译：基于文本挖掘和强化学习的蛋白质-蛋白质相互作用网络的构建及其在前列腺癌中的应用
5. Optimization and Machine Learning Methods for Diagnostic Testing of Prostate Cancer [D] . Merdan, Selin 2018

机译：前列腺癌诊断测试的优化和机器学习方法
6. LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer [O] . K. Babalyan, R. Sultanov, E. Generozov, -1

机译：LogLoss-BERAF：基于整体的机器学习模型用于构建高度精确的甲基化诊断集以解决前列腺癌的异质性
7. An accurate prostate cancer prognosticator using a seven-gene signature plus Gleason score and taking cell type heterogeneity into account. [O] . Xin Chen, Shizhong Xu, Michael McClelland, 2012

机译：准确的前列腺癌预后因子使用七基因签名加Gleason评分并考虑细胞类型异质性。

LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer

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