Detecting repeated cancer evolution from multi-region tumor sequencing data

Nature Methods

Volumn 15, Issue 9, 2018, Pages 707-714

  Caravagna, Giulio ^a,b   Giarratano, Ylenia ^b,c   Ramazzotti, Daniele ^d   Tomlinson, Ian ^e   Graham, Trevor A ^f   Sanguinetti, Guido ^b   Sottoriva, Andrea ^a  

^a INSTITUTE OF CANCER RESEARCH   (United Kingdom)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

^c UNIVERSITY OF EDINBURGH   (United Kingdom)

^d STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^e UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^f QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALLELE; ARTICLE; BREAST CANCER; CANCER DIAGNOSIS; CANCER RECURRENCE; CANCER SURVIVAL; COHORT ANALYSIS; COLORECTAL CANCER; CONTROLLED STUDY; DECISION TREE; GENE MUTATION; GENOMIC INSTABILITY; HUMAN; KIDNEY CANCER; MAJOR CLINICAL STUDY; MAXIMUM LIKELIHOOD METHOD; NON SMALL CELL LUNG CANCER; PHYLOGENETIC TREE; PRIORITY JOURNAL; TRANSFER OF LEARNING; WHOLE EXOME SEQUENCING; CLASSIFICATION; GENETICS; HIGH THROUGHPUT SEQUENCING; MACHINE LEARNING; MARKOV CHAIN; MOLECULAR EVOLUTION; NEOPLASM; PATHOLOGY; REPRODUCIBILITY; TUMOR CELL LINE;

CELL LINE, TUMOR; COHORT STUDIES; EVOLUTION, MOLECULAR; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MACHINE LEARNING; NEOPLASMS; REPRODUCIBILITY OF RESULTS; STOCHASTIC PROCESSES;

EID: 85052685030     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/s41592-018-0108-x     Document Type: Article

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