Algorithms and genome sequencing: Identifying driver pathways in cancer

Fabio Vandin; Eli Upfal; Benjamin J. Raphael

doi:10.1109/MC.2012.71

Algorithms and genome sequencing: Identifying driver pathways in cancer

Fabio Vandin, Eli Upfal, Benjamin J. Raphael

Research output: Contribution to specialist publication › Article

9 Scopus citations

Abstract

Two proposed algorithms predict which combinations of mutations in cancer genomes are priorities for experimental study. One relies on interaction network data to identify recurrently mutated sets of genes, while the other searches for groups of mutations that exhibit specific combinatorial properties.

Original language	English (US)
Pages	39-46
Number of pages	8
Volume	45
No	3
Specialist publication	Computer
DOIs	https://doi.org/10.1109/MC.2012.71
State	Published - Mar 2012
Externally published	Yes

All Science Journal Classification (ASJC) codes

General Computer Science

Keywords

Markov chain Monte Carlo
bioinformatics
cancer genomics
computational biology
diffusion processes
genome sequencing
networks

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10.1109/MC.2012.71

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title = "Algorithms and genome sequencing: Identifying driver pathways in cancer",

abstract = "Two proposed algorithms predict which combinations of mutations in cancer genomes are priorities for experimental study. One relies on interaction network data to identify recurrently mutated sets of genes, while the other searches for groups of mutations that exhibit specific combinatorial properties.",

keywords = "Markov chain Monte Carlo, bioinformatics, cancer genomics, computational biology, diffusion processes, genome sequencing, networks",

author = "Fabio Vandin and Eli Upfal and Raphael, {Benjamin J.}",

year = "2012",

month = mar,

doi = "10.1109/MC.2012.71",

language = "English (US)",

volume = "45",

pages = "39--46",

journal = "Computer",

issn = "0018-9162",

publisher = "IEEE Computer Society",

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T2 - Identifying driver pathways in cancer

AU - Vandin, Fabio

AU - Upfal, Eli

AU - Raphael, Benjamin J.

PY - 2012/3

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N2 - Two proposed algorithms predict which combinations of mutations in cancer genomes are priorities for experimental study. One relies on interaction network data to identify recurrently mutated sets of genes, while the other searches for groups of mutations that exhibit specific combinatorial properties.

AB - Two proposed algorithms predict which combinations of mutations in cancer genomes are priorities for experimental study. One relies on interaction network data to identify recurrently mutated sets of genes, while the other searches for groups of mutations that exhibit specific combinatorial properties.

KW - Markov chain Monte Carlo

KW - bioinformatics

KW - cancer genomics

KW - computational biology

KW - diffusion processes

KW - genome sequencing

KW - networks

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DO - 10.1109/MC.2012.71

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VL - 45

SP - 39

EP - 46

JO - Computer

JF - Computer

ER -

Algorithms and genome sequencing: Identifying driver pathways in cancer

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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