相似文献
1
survcomp: an R/Bioconductor package for performance assessment and comparison of survival models.
Bioinformatics. 2011 Nov 15;27(22):3206-8. doi: 10.1093/bioinformatics/btr511. Epub 2011 Sep 7.
2
A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?
Bioinformatics. 2008 Oct 1;24(19):2200-8. doi: 10.1093/bioinformatics/btn374. Epub 2008 Jul 17.
3
MSstatsQC 2.0: R/Bioconductor Package for Statistical Quality Control of Mass Spectrometry-Based Proteomics Experiments.
J Proteome Res. 2019 Feb 1;18(2):678-686. doi: 10.1021/acs.jproteome.8b00732. Epub 2018 Dec 14.
4
ROTS: An R package for reproducibility-optimized statistical testing.
PLoS Comput Biol. 2017 May 25;13(5):e1005562. doi: 10.1371/journal.pcbi.1005562. eCollection 2017 May.
5
periodR - an R package to calculate long-term cancer survival estimates using period analysis.
Methods Inf Med. 2009;48(2):123-8. doi: 10.3414/ME0563. Epub 2009 Feb 18.
6
mAPKL: R/ Bioconductor package for detecting gene exemplars and revealing their characteristics.
BMC Bioinformatics. 2015 Sep 15;16(1):291. doi: 10.1186/s12859-015-0719-5.
7
Software for the Integration of Multiomics Experiments in Bioconductor.
Cancer Res. 2017 Nov 1;77(21):e39-e42. doi: 10.1158/0008-5472.CAN-17-0344.
8
Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer.
J Biomed Inform. 2015 Aug;56:220-8. doi: 10.1016/j.jbi.2015.05.019. Epub 2015 Jun 3.
9
Assessment of performance of survival prediction models for cancer prognosis.
BMC Med Res Methodol. 2012 Jul 23;12:102. doi: 10.1186/1471-2288-12-102.
10
GUIDEseq: a bioconductor package to analyze GUIDE-Seq datasets for CRISPR-Cas nucleases.
BMC Genomics. 2017 May 15;18(1):379. doi: 10.1186/s12864-017-3746-y.
引用本文的文献
1
Identification of oncofetal PIWI-interacting RNAs as potential prognostic biomarkers in non-small cell lung cancer.
Front Genet. 2025 Aug 29;16:1611805. doi: 10.3389/fgene.2025.1611805. eCollection 2025.
2
Bayesian integration of longitudinal and survival outcomes in Alzheimer's disease prediction.
Alzheimers Dement. 2025 Sep;21(9):e70094. doi: 10.1002/alz.70094.
3
Development of a predictive model for long-term care and support eligibility certification associated with dementia-related functional impairments: the IRIDE cohort study.
BMC Public Health. 2025 Aug 21;25(1):2867. doi: 10.1186/s12889-025-23681-5.
4
OUTCOME-GUIDED DISEASE SUBTYPING BY GENERATIVE MODEL AND WEIGHTED JOINT LIKELIHOOD IN TRANSCRIPTOMIC APPLICATIONS.
Ann Appl Stat. 2024 Sep;18(3):1947-1964. doi: 10.1214/23-aoas1865. Epub 2024 Aug 5.
5
Validation of the simplified geriatric prognostic index in a Hellenic database of older patients (≥ 70 years old) with diffuse large B-cell lymphoma and similar disorders.
Ann Hematol. 2025 Jul 17. doi: 10.1007/s00277-025-06446-3.
6
Personalized risk stratification in colorectal cancer via PIANOS system.
Nat Commun. 2025 Jul 16;16(1):6561. doi: 10.1038/s41467-025-61713-1.
7
Multinational evaluation of AnthropoAge as a measure of biological age in the USA, England, Mexico, Costa Rica, and China: a population-based longitudinal study.
NPJ Aging. 2025 Jun 21;11(1):52. doi: 10.1038/s41514-025-00232-1.
8
Multisystem failure, tipping points, and risk of Alzheimer's disease.
Alzheimers Dement. 2025 May;21(5):e70249. doi: 10.1002/alz.70249.
9
Identification of a telomere-related gene signature for the prognostic and immune landscape prediction in head and neck squamous cell carcinoma by integrated analysis of machine learning and Mendelian randomization.
Medicine (Baltimore). 2025 Apr 18;104(16):e42211. doi: 10.1097/MD.0000000000042211.
10
metacp: a versatile software package for combining dependent or independent p-values.
BMC Bioinformatics. 2025 Apr 19;26(1):109. doi: 10.1186/s12859-025-06126-z.
本文引用的文献
1
On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.
Stat Med. 2011 May 10;30(10):1105-17. doi: 10.1002/sim.4154. Epub 2011 Jan 13.
2
SurvJamda: an R package to predict patients' survival and risk assessment using joint analysis of microarray gene expression data.
Bioinformatics. 2011 Apr 15;27(8):1168-9. doi: 10.1093/bioinformatics/btr103. Epub 2011 Mar 2.
3
Over-optimism in bioinformatics: an illustration.
Bioinformatics. 2010 Aug 15;26(16):1990-8. doi: 10.1093/bioinformatics/btq323. Epub 2010 Jun 26.
4
A fuzzy gene expression-based computational approach improves breast cancer prognostication.
Genome Biol. 2010;11(2):R18. doi: 10.1186/gb-2010-11-2-r18. Epub 2010 Feb 15.
5
Individual patient data meta-analysis of diagnostic and prognostic studies in obstetrics, gynaecology and reproductive medicine.
BMC Med Res Methodol. 2009 Mar 27;9:22. doi: 10.1186/1471-2288-9-22.
6
Parallelized prediction error estimation for evaluation of high-dimensional models.
Bioinformatics. 2009 Mar 15;25(6):827-9. doi: 10.1093/bioinformatics/btp062. Epub 2009 Jan 28.
7
A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?
Bioinformatics. 2008 Oct 1;24(19):2200-8. doi: 10.1093/bioinformatics/btn374. Epub 2008 Jul 17.
8
Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.
J Natl Cancer Inst. 2006 Feb 15;98(4):262-72. doi: 10.1093/jnci/djj052.
9
A new measure of prognostic separation in survival data.
Stat Med. 2004 Mar 15;23(5):723-48. doi: 10.1002/sim.1621.
10
Forest plots: trying to see the wood and the trees.
BMJ. 2001 Jun 16;322(7300):1479-80. doi: 10.1136/bmj.322.7300.1479.
Zotero 插件