SSizer: Determining the Sample Sufficiency for Comparative Biological Study.
暂无分享,去创建一个
Yang Zhang | Ying Zhou | Feng Zhu | Jie Hu | Yunqing Qiu | Bo Yang | Yongchao Luo | Jing Tang | Weiwei Xue | Qiaojun He | Fengcheng Li | Xiaoyu Zhang | Qingxia Yang | Qiaojun He | Weiwei Xue | Feng Zhu | Jing Tang | Yongchao Luo | Qingxia Yang | Yang Zhang | Fengcheng Li | Xiaoyu Zhang | Ying Zhou | Jie Hu | Bo Yang | Yunqing Qiu
[1] Tsuyoshi Murata,et al. {m , 1934, ACML.
[2] Yu Guo,et al. Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms , 2010, BMC Bioinformatics.
[3] J. Eng,et al. Sample Size Estimation : How Many Individuals Should Be Studied ? , 2022 .
[4] Yudong D. He,et al. Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.
[5] Basten L. Snoek,et al. Transcriptome profiling of Ricinus communis L. provides new insights underlying the mechanisms towards thermotolerance during seed imbibition and germination , 2018, Industrial Crops and Products.
[6] Eytan Domany,et al. Using high-throughput transcriptomic data for prognosis: a critical overview and perspectives. , 2014, Cancer research.
[7] A. Carroll,et al. Untargeted NMR-based metabolomics for field-scale monitoring: Temporal reproducibility and biomarker discovery in mosquitofish (Gambusia holbrooki) from a metal(loid)-contaminated wetland. , 2018, Environmental pollution.
[8] L. Ein-Dor,et al. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[9] Lisa N Yelland,et al. Accounting for twin births in sample size calculations for randomised trials , 2018, Paediatric and perinatal epidemiology.
[10] Tingting Fu,et al. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics , 2017, Nucleic Acids Res..
[11] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[12] J. Friedman. Regularized Discriminant Analysis , 1989 .
[13] William Fenical,et al. Comparative transcriptomics as a guide to natural product discovery and biosynthetic gene cluster functionality , 2017, Proceedings of the National Academy of Sciences.
[14] Qing Zeng-Treitler,et al. Predicting sample size required for classification performance , 2012, BMC Medical Informatics and Decision Making.
[15] Feng Zhu,et al. Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs , 2019, Briefings Bioinform..
[16] Feng Zhu,et al. Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains* , 2019, Molecular & Cellular Proteomics.
[17] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[18] Van,et al. A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.
[19] Josep Ramon Marsal,et al. Bin-CE: A comprehensive web application to decide upon the best set of outcomes to be combined in a binary composite endpoint , 2018, PloS one.
[21] R. Gibbs,et al. Comparative genomics of the miniature wasp and pest control agent Trichogramma pretiosum , 2018, BMC Biology.
[22] J. Ioannidis. Microarrays and molecular research: noise discovery? , 2005, The Lancet.
[23] R. Hayward,et al. Type II (β) errors in the hand literature: The importance of power , 1998 .
[24] Danna Zhou,et al. d. , 1934, Microbial pathogenesis.
[25] Olivier Thas,et al. On determining the power of digital PCR experiments , 2018, Analytical and Bioanalytical Chemistry.
[26] Samantha F Anderson,et al. Best (but oft forgotten) practices: sample size planning for powerful studies. , 2019, The American journal of clinical nutrition.
[27] K. Strimbu,et al. What are biomarkers? , 2010, Current opinion in HIV and AIDS.
[28] Xiaofeng Li,et al. ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies , 2019, Briefings Bioinform..
[29] A. Nobel,et al. Concordance among Gene-Expression – Based Predictors for Breast Cancer , 2011 .
[30] David S. Wishart,et al. MetaboAnalyst 3.0—making metabolomics more meaningful , 2015, Nucleic Acids Res..
[31] P. Visscher,et al. OSCA: a tool for omic-data-based complex trait analysis , 2018, Genome Biology.
[32] Bauke Ylstra,et al. CGHpower: exploring sample size calculations for chromosomal copy number experiments , 2010, BMC Bioinformatics.
[33] Bo Li,et al. NOREVA: normalization and evaluation of MS-based metabolomics data , 2017, Nucleic Acids Res..
[34] Christoph Steinbeck,et al. MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data , 2012, Nucleic Acids Res..
[35] Yan Guo,et al. RnaSeqSampleSize: real data based sample size estimation for RNA sequencing , 2018, BMC Bioinformatics.
[36] Feng Zhu,et al. Assessing the Effectiveness of Direct Data Merging Strategy in Long-Term and Large-Scale Pharmacometabonomics , 2019, Front. Pharmacol..
[37] Gokmen Zararsiz,et al. easyROC: An Interactive Web-tool for ROC Curve Analysis Using R Language Environment , 2016, R J..
[38] David P. Kreil,et al. The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance , 2014, Nature Biotechnology.
[39] J. Foekens,et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.
[40] Sean R. Davis,et al. NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..
[41] Elaine Holmes,et al. Power Analysis and Sample Size Determination in Metabolic Phenotyping. , 2016, Analytical chemistry.
[42] A. Thoma,et al. A Systematic Review of Power and Sample Size Reporting in Randomized Controlled Trials within Plastic Surgery , 2012, Plastic and reconstructive surgery.
[43] N. Chandra,et al. Glycomics and Proteomics Approaches to Investigate Early Adenovirus–Host Cell Interactions , 2018, Journal of Molecular Biology.
[44] M. Zheng,et al. High-resolution length fractionation of surfactant-dispersed carbon nanotubes. , 2013, Analytical chemistry.
[45] J. Koenderink. Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.
[46] Xiaofeng Li,et al. Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data , 2019, Briefings Bioinform..
[47] Brian A. Nosek,et al. Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.
[48] M. van Iterson,et al. Relative power and sample size analysis on gene expression profiling data , 2009, BMC Genomics.
[49] Jana Novovicová,et al. Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[50] S. Dorus,et al. Comparative Sperm Proteomics in Mouse Species with Divergent Mating Systems , 2017, Molecular biology and evolution.
[51] P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .
[52] D. Wishart,et al. Translational biomarker discovery in clinical metabolomics: an introductory tutorial , 2012, Metabolomics.
[53] Feng Zhu,et al. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics , 2019, Nucleic Acids Res..
[54] Vincent Navratil,et al. Sample size calculation in metabolic phenotyping studies , 2015, Briefings Bioinform..
[55] P. Visscher,et al. Calculating statistical power in Mendelian randomization studies. , 2013, International journal of epidemiology.
[56] V M Eguíluz,et al. The importance of sample size in marine megafauna tagging studies. , 2019, Ecological applications : a publication of the Ecological Society of America.
[57] Martin Eisenacher,et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data , 2018, Nucleic Acids Res..
[58] Feng Zhu,et al. VARIDT 1.0: variability of drug transporter database , 2019, Nucleic Acids Res..
[59] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[60] Andrew E Teschendorff,et al. Avoiding common pitfalls in machine learning omic data science , 2018, Nature Materials.
[61] Thomas Lengauer,et al. ROCR: visualizing classifier performance in R , 2005, Bioinform..
[62] Jonathan Terhorst,et al. U-PASS: unified power analysis and forensics for qualitative traits in genetic association studies , 2019, bioRxiv.