Algorithmic methods to infer the evolutionary trajectories in cancer progression

Proceedings of the National Academy of Sciences of the United States of America

Volumn 113, Issue 28, 2016, Pages E4025-E4034

  Caravagna, Giulio ^a,b   Graudenzi, Alex ^a,c   Ramazzotti, Daniele ^a   Sanz Pamplona, Rebeca ^d   De Sano, Luca ^a   Mauri, Giancarlo ^a,e   Moreno, Victor ^d,f   Antoniotti, Marco ^a   Mishra, Bud ^g  

^a UNIVERSITY OF MILANO BICOCCA   (Italy)

^b UNIVERSITY OF EDINBURGH   (United Kingdom)

^c INSTITUTE OF MOLECULAR BIOIMAGING AND PHYSIOLOGY   (Italy)

^d HOSPITAL UNIVERSITARI DE BELLVITGE   (Spain)

^e SYSBIO Centre of Systems Biology (SYSBIO)   (Italy)

^f UNIVERSITY OF BARCELONA   (Spain)

^g NEW YORK UNIVERSITY   (United States)

Author keywords

Bayesian structural inference; Cancer evolution; Causality; Next generation sequencing; Selective advantage

Indexed keywords

APC PROTEIN; B RAF KINASE; BETA CATENIN; K RAS PROTEIN; MITOGEN ACTIVATED PROTEIN KINASE; PHOSPHATIDYLINOSITOL 3 KINASE; PHOSPHATIDYLINOSITOL 3,4,5 TRISPHOSPHATE 3 PHOSPHATASE; PROTEIN P53; RETINOBLASTOMA BINDING PROTEIN 2; SMAD4 PROTEIN; TRANSCRIPTION FACTOR 7 LIKE 2; TRANSCRIPTION FACTOR SOX; TRANSCRIPTION FACTOR SOX9; WNT PROTEIN; MICROSATELLITE DNA;

ALGORITHM; ARTICLE; CANCER GROWTH; CLINICAL EVALUATION; COLON CARCINOGENESIS; COLORECTAL CANCER; GENE MUTATION; GENE SEQUENCE; GENETIC HETEROGENEITY; GENOME ANALYSIS; HUMAN; ILLNESS TRAJECTORY; MACHINE LEARNING; MOLECULAR EVOLUTION; NEXT GENERATION SEQUENCING; PIPELINE FOR CANCER INFERENCE; POPULATION; PRIORITY JOURNAL; STRATIFIED SAMPLE; TUMOR GENE; BIOLOGICAL MODEL; COLORECTAL TUMOR; EVOLUTION; GENETICS;

ALGORITHMS; BIOLOGICAL EVOLUTION; COLORECTAL NEOPLASMS; HUMANS; MACHINE LEARNING; MICROSATELLITE REPEATS; MODELS, GENETIC;

EID: 84978107412     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1520213113     Document Type: Article

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