VariBench, new variation benchmark categories and data sets.
作者信息
Shirvanizadeh Niloofar, Vihinen Mauno
机构信息
Department of Experimental Medical Science, Lund University, Lund, Sweden.
出版信息
Front Bioinform. 2023 Sep 19;3:1248732. doi: 10.3389/fbinf.2023.1248732. eCollection 2023.
Abstract
摘要
相似文献
1
VariBench, new variation benchmark categories and data sets.VariBench，新的变异基准类别和数据集。
Front Bioinform. 2023 Sep 19;3:1248732. doi: 10.3389/fbinf.2023.1248732. eCollection 2023.
2
VariBench: a benchmark database for variations.VariBench：一个变异基准数据库。
Hum Mutat. 2013 Jan;34(1):42-9. doi: 10.1002/humu.22204. Epub 2012 Oct 11.
3
Variation benchmark datasets: update, criteria, quality and applications.变异基准数据集：更新、标准、质量和应用。
Database (Oxford). 2020 Jan 1;2020. doi: 10.1093/database/baz117.
4
Representativeness of variation benchmark datasets.变异性基准数据集的代表性。
BMC Bioinformatics. 2018 Nov 29;19(1):461. doi: 10.1186/s12859-018-2478-6.
5
How to evaluate performance of prediction methods? Measures and their interpretation in variation effect analysis.如何评估预测方法的性能？变异效应分析中的度量及其解释。
BMC Genomics. 2012 Jun 18;13 Suppl 4(Suppl 4):S2. doi: 10.1186/1471-2164-13-S4-S2.
6
Impact of benchmark data set topology on the validation of virtual screening methods: exploration and quantification by spatial statistics.基准数据集拓扑结构对虚拟筛选方法验证的影响：通过空间统计进行探索和量化
J Chem Inf Model. 2008 Apr;48(4):704-18. doi: 10.1021/ci700099u. Epub 2008 Apr 2.
7
PON-P2: prediction method for fast and reliable identification of harmful variants.PON-P2：快速可靠识别有害变异的预测方法
PLoS One. 2015 Feb 3;10(2):e0117380. doi: 10.1371/journal.pone.0117380. eCollection 2015.
8
An integrated Asian human SNV and indel benchmark established using multiple sequencing methods.利用多种测序方法建立的综合亚洲人类单核苷酸变异和插入缺失基准。
Sci Rep. 2020 Jun 17;10(1):9821. doi: 10.1038/s41598-020-66605-6.
9
Developing a benchmark for emotional analysis of music.建立音乐情感分析的基准。
PLoS One. 2017 Mar 10;12(3):e0173392. doi: 10.1371/journal.pone.0173392. eCollection 2017.
10
dbCPM: a manually curated database for exploring the cancer passenger mutations.dbCPM：一个用于探索癌症乘客突变的人工整理数据库。
Brief Bioinform. 2020 Jan 17;21(1):309-317. doi: 10.1093/bib/bby105.
引用本文的文献
1
A curated benchmark dataset for molecular identification based on genome skimming.一个基于基因组浅层测序的用于分子鉴定的精选基准数据集。
Sci Data. 2025 May 29;12(1):906. doi: 10.1038/s41597-025-05230-2.
2
Identification of positions in human aldolase a that are neutral for apparent K.鉴定人醛缩酶 a 中对表观 K. 为中性的位置。
Arch Biochem Biophys. 2024 Nov;761:110183. doi: 10.1016/j.abb.2024.110183. Epub 2024 Oct 24.
本文引用的文献
1
Nonsynonymous Synonymous Variants Demand for a Paradigm Shift in Genetics.非同义与同义变异：遗传学范式转变的需求
Curr Genomics. 2023 Jun 23;24(1):18-23. doi: 10.2174/1389202924666230417101020.
2
Systematic errors in annotations of truncations, loss-of-function and synonymous variants.截短突变、功能丧失突变和同义突变注释中的系统误差。
Front Genet. 2023 Jan 13;14:1015017. doi: 10.3389/fgene.2023.1015017. eCollection 2023.
3
When a Synonymous Variant Is Nonsynonymous.同义突变非同义。
Genes (Basel). 2022 Aug 19;13(8):1485. doi: 10.3390/genes13081485.
4
PON-All: Amino Acid Substitution Tolerance Predictor for All Organisms.PON-All：所有生物的氨基酸替换耐受性预测工具
Front Mol Biosci. 2022 Jun 16;9:867572. doi: 10.3389/fmolb.2022.867572. eCollection 2022.
5
The complete sequence of a human genome.人类基因组的完整序列。
Science. 2022 Apr;376(6588):44-53. doi: 10.1126/science.abj6987. Epub 2022 Mar 31.
6
An expanded phenotype centric benchmark of variant prioritisation tools.一种基于扩展表型的变异优先级工具基准测试。
Hum Mutat. 2022 May;43(5):539-546. doi: 10.1002/humu.24362. Epub 2022 Mar 9.
7
Machine learning models for accurate prioritization of variants of uncertain significance.用于准确确定意义未明变异优先级的机器学习模型。
Hum Mutat. 2022 Apr;43(4):449-460. doi: 10.1002/humu.24339. Epub 2022 Feb 19.
8
Analysis of missense variants in the human genome reveals widespread gene-specific clustering and improves prediction of pathogenicity.分析人类基因组中的错义变异揭示了广泛的基因特异性聚类，并提高了致病性预测的准确性。
Am J Hum Genet. 2022 Mar 3;109(3):457-470. doi: 10.1016/j.ajhg.2022.01.006. Epub 2022 Feb 3.
9
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.TP53_PROF：一种用于预测 TP53 错义突变影响的机器学习模型。
Brief Bioinform. 2022 Mar 10;23(2). doi: 10.1093/bib/bbab524.
10
StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants.StrVCTVRE：一种用于预测人类基因组结构变异致病性的监督学习方法。
Am J Hum Genet. 2022 Feb 3;109(2):195-209. doi: 10.1016/j.ajhg.2021.12.007. Epub 2022 Jan 14.
Zotero 插件