ICBioMark: Data-Driven Design of Targeted Gene Panels for Estimating Immunotherapy Biomarkers

Jacob R. Bradley; Timothy I Cannings

ICBioMark: Data-Driven Design of Targeted Gene Panels for Estimating Immunotherapy Biomarkers

School of Mathematics

Research output: Non-textual form › Software

Abstract

Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.

Original language	English
Publisher	CRAN - The Comprehensive R Archive Network
Publication status	Published - 18 Mar 2021

Access to Document

https://cran.r-project.org/package=ICBioMarkLicence: Other

1 Finished

Statistical learning techniques for genomic medicine
Cannings, T. (Principal Investigator) & Myant, K. (Co-investigator)
UK industry, commerce and public corporations
1/09/19 → 31/08/23
Project: Research

1 Article

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers
Bradley, J. R. & Cannings, T. I., 23 Feb 2022, In: Communications Biology. 5, 156.
Research output: Contribution to journal › Article › peer-review

Open Access
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Cite this

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title = "ICBioMark: Data-Driven Design of Targeted Gene Panels for Estimating Immunotherapy Biomarkers",

abstract = "Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) . This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.",

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T2 - Data-Driven Design of Targeted Gene Panels for Estimating Immunotherapy Biomarkers

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N2 - Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) . This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.

AB - Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) . This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.

M3 - Software

PB - CRAN - The Comprehensive R Archive Network

ER -

ICBioMark: Data-Driven Design of Targeted Gene Panels for Estimating Immunotherapy Biomarkers

Abstract

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Projects

Statistical learning techniques for genomic medicine

Research output

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

Cite this