• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

迈向更好的基准测试:癌症基因组学中基于挑战的方法评估

Toward better benchmarking: challenge-based methods assessment in cancer genomics.

作者信息

Boutros Paul C, Margolin Adam A, Stuart Joshua M, Califano Andrea, Stolovitzky Gustavo

出版信息

Genome Biol. 2014 Sep 17;15(9):462. doi: 10.1186/s13059-014-0462-7.

DOI:10.1186/s13059-014-0462-7
PMID:25314947
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4318527/
Abstract

Rapid technological development has created an urgent need for improved evaluation of algorithms for the analysis of cancer genomics data. We outline how challenge-based assessment may help fill this gap by leveraging crowd-sourcing to distribute effort and reduce bias.

摘要

快速的技术发展使得迫切需要改进对癌症基因组学数据分析算法的评估。我们概述了基于挑战的评估如何通过利用众包来分散工作量并减少偏差,从而有助于填补这一空白。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35db/4318527/a149715b2ed7/13059_2014_462_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35db/4318527/d6afd1963902/13059_2014_462_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35db/4318527/a149715b2ed7/13059_2014_462_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35db/4318527/d6afd1963902/13059_2014_462_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/35db/4318527/a149715b2ed7/13059_2014_462_Fig2_HTML.jpg

相似文献

1
Toward better benchmarking: challenge-based methods assessment in cancer genomics.迈向更好的基准测试:癌症基因组学中基于挑战的方法评估
Genome Biol. 2014 Sep 17;15(9):462. doi: 10.1186/s13059-014-0462-7.
2
Whole-genome sequencing analysis of CNV using low-coverage and paired-end strategies is efficient and outperforms array-based CNV analysis.采用低覆盖度和双端测序策略进行全基因组测序分析,效率高,优于基于阵列的 CNV 分析。
J Med Genet. 2018 Nov;55(11):735-743. doi: 10.1136/jmedgenet-2018-105272. Epub 2018 Jul 30.
3
Clinical Validation of a Next-Generation Sequencing Genomic Oncology Panel via Cross-Platform Benchmarking against Established Amplicon Sequencing Assays.通过与既定的扩增子测序检测进行跨平台基准测试对新一代测序基因组肿瘤学检测板进行临床验证。
J Mol Diagn. 2017 Jan;19(1):43-56. doi: 10.1016/j.jmoldx.2016.07.012. Epub 2016 Nov 9.
4
Genomic Amplifications Cause False Positives in CRISPR Screens.基因组扩增在CRISPR筛选中导致假阳性。
Cancer Discov. 2016 Aug;6(8):824-6. doi: 10.1158/2159-8290.CD-16-0665.
5
Pricey cancer genome project struggles with sample shortage.昂贵的癌症基因组计划因样本短缺而陷入困境。
Nat Med. 2007 Apr;13(4):391. doi: 10.1038/nm0407-391. Epub 2007 Mar 28.
6
Cancer: drivers and passengers.癌症:驱动因素与乘客因素
Nature. 2007 Mar 8;446(7132):145-6. doi: 10.1038/446145a.
7
Systematic benchmarking of omics computational tools.系统生物学计算工具的基准测试。
Nat Commun. 2019 Mar 27;10(1):1393. doi: 10.1038/s41467-019-09406-4.
8
Avoiding the pitfalls of gene set enrichment analysis with SetRank.使用SetRank避免基因集富集分析的陷阱。
BMC Bioinformatics. 2017 Mar 4;18(1):151. doi: 10.1186/s12859-017-1571-6.
9
Design and analysis issues in genome-wide somatic mutation studies of cancer.癌症全基因组体细胞突变研究中的设计与分析问题
Genomics. 2009 Jan;93(1):17-21. doi: 10.1016/j.ygeno.2008.07.005. Epub 2008 Aug 23.
10
Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection.将肿瘤基因组模拟与众包相结合,以评估体细胞单核苷酸变异检测。
Nat Methods. 2015 Jul;12(7):623-30. doi: 10.1038/nmeth.3407. Epub 2015 May 18.

引用本文的文献

1
ViLoN-a multi-layer network approach to data integration demonstrated for patient stratification.ViLoN——一种用于数据集成的多层网络方法,用于患者分层。
Nucleic Acids Res. 2023 Jan 11;51(1):e6. doi: 10.1093/nar/gkac988.
2
Training undergraduate research assistants with an outcome-oriented and skill-based mentoring strategy.采用以结果为导向和基于技能的指导策略培训本科生研究助理。
Acta Crystallogr D Struct Biol. 2022 Aug 1;78(Pt 8):936-944. doi: 10.1107/S2059798322005861. Epub 2022 Jul 14.
3
Essential guidelines for computational method benchmarking.

本文引用的文献

1
Global optimization of somatic variant identification in cancer genomes with a global community challenge.通过全球社区挑战对癌症基因组中的体细胞变异识别进行全局优化。
Nat Genet. 2014 Apr;46(4):318-319. doi: 10.1038/ng.2932.
2
An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge.在“清晰度挑战”中,为制定临床基因组测序结果分析、解读和报告的最佳实践标准而开展的一项国际努力。
Genome Biol. 2014 Mar 25;15(3):R53. doi: 10.1186/gb-2014-15-3-r53.
3
RNA design rules from a massive open laboratory.
计算方法基准测试的基本指南。
Genome Biol. 2019 Jun 20;20(1):125. doi: 10.1186/s13059-019-1738-8.
4
Calling Variants in the Clinic: Informed Variant Calling Decisions Based on Biological, Clinical, and Laboratory Variables.临床中的变异检测:基于生物学、临床和实验室变量做出明智的变异检测决策
Comput Struct Biotechnol J. 2019 Apr 8;17:561-569. doi: 10.1016/j.csbj.2019.04.002. eCollection 2019.
5
Systematic benchmarking of omics computational tools.系统生物学计算工具的基准测试。
Nat Commun. 2019 Mar 27;10(1):1393. doi: 10.1038/s41467-019-09406-4.
6
Why rankings of biomedical image analysis competitions should be interpreted with care.为什么要谨慎解读生物医学图像分析竞赛的排名。
Nat Commun. 2018 Dec 6;9(1):5217. doi: 10.1038/s41467-018-07619-7.
7
Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection.结合精确的肿瘤基因组模拟和众包基准测试体细胞结构变异检测。
Genome Biol. 2018 Nov 6;19(1):188. doi: 10.1186/s13059-018-1539-5.
8
Valection: design optimization for validation and verification studies.选择法:验证和确认研究的设计优化。
BMC Bioinformatics. 2018 Sep 25;19(1):339. doi: 10.1186/s12859-018-2391-z.
9
Convolutional neural network scoring and minimization in the D3R 2017 community challenge.卷积神经网络评分和最小化在 D3R 2017 社区挑战赛中。
J Comput Aided Mol Des. 2019 Jan;33(1):19-34. doi: 10.1007/s10822-018-0133-y. Epub 2018 Jul 10.
10
geck: trio-based comparative benchmarking of variant calls.geck:基于 trio 的变异调用比较基准测试。
Bioinformatics. 2018 Oct 15;34(20):3488-3495. doi: 10.1093/bioinformatics/bty415.
从大规模开放实验室中获得的 RNA 设计规则。
Proc Natl Acad Sci U S A. 2014 Feb 11;111(6):2122-7. doi: 10.1073/pnas.1313039111. Epub 2014 Jan 27.
4
Assessment of transcript reconstruction methods for RNA-seq.RNA-seq 转录本重构方法评估。
Nat Methods. 2013 Dec;10(12):1177-84. doi: 10.1038/nmeth.2714. Epub 2013 Nov 3.
5
Taking pan-cancer analysis global.放眼全球,开展泛癌症分析。
Nat Genet. 2013 Nov;45(11):1263. doi: 10.1038/ng.2825.
6
Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas.实现癌症基因组图谱中 12 种肿瘤类型的透明和协作计算分析。
Nat Genet. 2013 Oct;45(10):1121-6. doi: 10.1038/ng.2761.
7
Reproducibility of high-throughput mRNA and small RNA sequencing across laboratories.高通量 mRNA 和 small RNA 测序在实验室间的可重复性。
Nat Biotechnol. 2013 Nov;31(11):1015-22. doi: 10.1038/nbt.2702. Epub 2013 Sep 15.
8
Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge.基于微阵列的表型预测的优势和局限性:从 IMPROVER 诊断特征挑战中吸取的经验教训。
Bioinformatics. 2013 Nov 15;29(22):2892-9. doi: 10.1093/bioinformatics/btt492. Epub 2013 Aug 20.
9
A Turing test for artificial expression data.人工表达数据的图灵测试。
Bioinformatics. 2013 Oct 15;29(20):2603-9. doi: 10.1093/bioinformatics/btt438. Epub 2013 Aug 16.
10
Assemblathon 2: evaluating de novo methods of genome assembly in three vertebrate species.Assemblathon2:在三个脊椎动物物种中评估从头组装基因组方法。
Gigascience. 2013 Jul 22;2(1):10. doi: 10.1186/2047-217X-2-10.