Paper intensive reading (十七)：Methods that remove batch effects may lead to exaggerated confidence

時間 2021-01-02

標籤 Paper Reading 简体版

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論文題目：Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses scholar 引用：100 頁數：11 發表時間：August 13, 2015 發表刊物：Biostatistics 作者：VEGAR

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1. Paper intensive reading (十四)：Batch effects in single-cell RNA-sequencing data are corrected
2. Paper intensive reading (十六)：Deep learning enables accurate clustering and batch effect removal
3. Paper reading (四十四)： Machine learning methods for metabolic pathway prediction
4. Paper reading (五十)：Supervised prediction of drug-induced nephrotoxicity
5. Paper reading (五十七)：MetaNN accurate classification of host phenotypes from metagenomic data using NN
6. Paper intensive reading (九)：Toll-Like Receptor 7 Agonist GS-9620 Induces Prolonged Inhibition of HBV
7. Paper reading (七十五)：I sense overeating: Motif-based machine learning framework to detect overeating
8. Paper intensive reading (五)：A Review on Role of Microbiome in Obesity and Antiobesity Properties
9. Paper Reading: Slimmable Neural Networks
10. [paper reading] DenseNet

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