Guided Visual Exploration of Genomic Stratifications in Cancer

Marc Streit; A. Lex; Samuel Gratzl; C. Partl; D. Schmalstieg; HP Pfister

doi:10.1038/nmeth.3088

Guided Visual Exploration of Genomic Stratifications in Cancer

Marc Streit, A. Lex, Samuel Gratzl, C. Partl, D. Schmalstieg, HP Pfister

Institute of Computer Graphics

Research output: Contribution to journal › Article › peer-review

Abstract

Cancer is a heterogeneous disease, and molecular profiling of tumors from large cohorts has enabled characterization of new tumor subtypes. This is a prerequisite for improving personalized treatment and ultimately achieving better patient outcomes. Potential tumor subtypes can be identified with methods such as unsupervised clustering1 or network-based stratification2, which assign patients to sets based on high-dimensional molecular profiles. Detailed characterization of identified sets and their interpretation, however, remain a time-consuming exploratory process.

Original language	English
Pages (from-to)	884-885
Number of pages	2
Journal	Nature Methods
Volume	11
Issue number	9
DOIs	https://doi.org/10.1038/nmeth.3088
Publication status	Published - Sept 2014

Fields of science

102 Computer Sciences
102003 Image processing
102008 Computer graphics
102015 Information systems
102020 Medical informatics
103021 Optics

JKU Focus areas

Engineering and Natural Sciences (in general)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1038/nmeth.3088

Cite this

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title = "Guided Visual Exploration of Genomic Stratifications in Cancer",

abstract = "Cancer is a heterogeneous disease, and molecular profiling of tumors from large cohorts has enabled characterization of new tumor subtypes. This is a prerequisite for improving personalized treatment and ultimately achieving better patient outcomes. Potential tumor subtypes can be identified with methods such as unsupervised clustering1 or network-based stratification2, which assign patients to sets based on high-dimensional molecular profiles. Detailed characterization of identified sets and their interpretation, however, remain a time-consuming exploratory process.",

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AU - Streit, Marc

AU - Lex, A.

AU - Gratzl, Samuel

AU - Partl, C.

AU - Schmalstieg, D.

AU - Pfister, HP

PY - 2014/9

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AB - Cancer is a heterogeneous disease, and molecular profiling of tumors from large cohorts has enabled characterization of new tumor subtypes. This is a prerequisite for improving personalized treatment and ultimately achieving better patient outcomes. Potential tumor subtypes can be identified with methods such as unsupervised clustering1 or network-based stratification2, which assign patients to sets based on high-dimensional molecular profiles. Detailed characterization of identified sets and their interpretation, however, remain a time-consuming exploratory process.

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DO - 10.1038/nmeth.3088

M3 - Article

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JO - Nature Methods

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