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临床环境中可视化研究的棘手复杂性:来自基因组学的案例研究。

The thorny complexities of visualization research for clinical settings: A case study from genomics.

作者信息

Ståhlbom Emilia, Molin Jesper, Ynnerman Anders, Lundström Claes

机构信息

Department of Science and Technology, Linköping University, Linköping, Sweden.

Sectra AB, Linköping, Sweden.

出版信息

Front Bioinform. 2023 Mar 29;3:1112649. doi: 10.3389/fbinf.2023.1112649. eCollection 2023.

DOI:10.3389/fbinf.2023.1112649
PMID:37063648
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10090312/
Abstract

In this perspective article we discuss a certain type of research on visualization for bioinformatics data, namely, methods targeting clinical use. We argue that in this subarea additional complex challenges come into play, particularly so in genomics. We here describe four such challenge areas, elicited from a domain characterization effort in clinical genomics. We also list opportunities for visualization research to address clinical challenges in genomics that were uncovered in the case study. The findings are shown to have parallels with experiences from the diagnostic imaging domain.

摘要

在这篇观点文章中,我们讨论了一种针对生物信息学数据可视化的特定类型研究,即针对临床应用的方法。我们认为,在这个子领域中会出现额外的复杂挑战,尤其是在基因组学方面。我们在此描述了从临床基因组学领域特征分析工作中引出的四个此类挑战领域。我们还列出了可视化研究在应对案例研究中发现的基因组学临床挑战方面的机会。研究结果显示与诊断成像领域的经验有相似之处。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87dc/10090312/583b8e0b827a/fbinf-03-1112649-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87dc/10090312/78e46da7e7bb/fbinf-03-1112649-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87dc/10090312/583b8e0b827a/fbinf-03-1112649-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87dc/10090312/78e46da7e7bb/fbinf-03-1112649-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87dc/10090312/583b8e0b827a/fbinf-03-1112649-g002.jpg

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本文引用的文献

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CNV-ClinViewer: enhancing the clinical interpretation of large copy-number variants online.CNV-ClinViewer:在线增强对大型拷贝数变异的临床解读。
Bioinformatics. 2023 May 4;39(5). doi: 10.1093/bioinformatics/btad290.
2
GenoREC: A Recommendation System for Interactive Genomics Data Visualization.GenoREC:交互式基因组学数据可视化推荐系统。
IEEE Trans Vis Comput Graph. 2023 Jan;29(1):570-580. doi: 10.1109/TVCG.2022.3209407. Epub 2022 Dec 21.
3
Multi-View Design Patterns and Responsive Visualization for Genomics Data.多视图设计模式与基因组学数据的响应式可视化
IEEE Trans Vis Comput Graph. 2023 Jan;29(1):559-569. doi: 10.1109/TVCG.2022.3209398. Epub 2022 Dec 21.
4
Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?医学人工智能在放射科的应用:由谁决定以及如何决定?
Radiology. 2022 Dec;305(3):555-563. doi: 10.1148/radiol.212151. Epub 2022 Aug 2.
5
CNViz: An R/Shiny Application for Interactive Copy Number Variant Visualization in Cancer.CNViz:一款用于癌症中拷贝数变异交互式可视化的R/Shiny应用程序。
J Pathol Inform. 2022 Feb 15;13:100089. doi: 10.1016/j.jpi.2022.100089. eCollection 2022.
6
Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding".用于诊断的下一代人工智能:从预测诊断标签到“路径导航”。
JAMA. 2021 Dec 28;326(24):2467-2468. doi: 10.1001/jama.2021.22396.
7
Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization.高辛烷值烷烃:用于可扩展和交互式基因组学数据可视化的基于语法的工具包。
IEEE Trans Vis Comput Graph. 2022 Jan;28(1):140-150. doi: 10.1109/TVCG.2021.3114876. Epub 2021 Dec 30.
8
User testing of a diagnostic decision support system with machine-assisted chart review to facilitate clinical genomic diagnosis.使用机器辅助图表审查对诊断决策支持系统进行用户测试,以促进临床基因组诊断。
BMJ Health Care Inform. 2021 May;28(1). doi: 10.1136/bmjhci-2021-100331.
9
Exploration of Coding and Non-coding Variants in Cancer Using GenomePaint.使用GenomePaint探索癌症中的编码和非编码变异
Cancer Cell. 2021 Jan 11;39(1):83-95.e4. doi: 10.1016/j.ccell.2020.12.011.
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inCNV: An Integrated Analysis Tool for Copy Number Variation on Whole Exome Sequencing.inCNV:全外显子组测序中拷贝数变异的综合分析工具。
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