Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma

Cell Systems

Volumn 5, Issue 6, 2017, Pages 620-627.e3

  Yu, Kun Hsing ^a,b   Berry, Gerald J ^c   Rubin, Daniel L ^a   Ré, Christopher ^d   Altman, Russ B ^a,d   Snyder, Michael ^a  

^a STANFORD UNIVERSITY   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d Stanford University   (United States)

Author keywords

cancer genomics; cancer imaging; cancer proteomics; cancer transcriptomics; lung adenocarcinoma; machine learning; precision medicine; predictive medicine; quantitative pathology

Indexed keywords

ARTICLE; CANCER DIAGNOSIS; CANCER PROGNOSIS; CANCER STAGING; CANCER THERAPY; CELL CYCLE REGULATION; CELL DEDIFFERENTIATION; CELL STRUCTURE; CLINICAL FEATURE; CONTROLLED STUDY; DISEASE ASSOCIATION; GENE EXPRESSION; GENETIC VARIABILITY; HISTOPATHOLOGY; HUMAN; LUNG ADENOCARCINOMA; MAJOR CLINICAL STUDY; PRIORITY JOURNAL; PROTEOMICS; RNA SEQUENCE; SURVIVAL PREDICTION; TRANSCRIPTOMICS; TUMOR CELL; WHOLE EXOME SEQUENCING; ADENOCARCINOMA; AGED; BIOLOGICAL MODEL; CELL CYCLE; DECISION SUPPORT SYSTEM; FEMALE; GENETICS; LUNG; LUNG TUMOR; MACHINE LEARNING; MALE; MIDDLE AGED; PATHOLOGY; PERSONALIZED MEDICINE; PROGNOSIS;

TRANSCRIPTOME;

ADENOCARCINOMA; AGED; CELL CYCLE; CELL DEDIFFERENTIATION; DECISION MAKING, COMPUTER-ASSISTED; FEMALE; HUMANS; LUNG; LUNG NEOPLASMS; MACHINE LEARNING; MALE; MIDDLE AGED; MODELS, BIOLOGICAL; NEOPLASM STAGING; PRECISION MEDICINE; PROGNOSIS; PROTEOMICS; TRANSCRIPTOME;

EID: 85033780544     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2017.10.014     Document Type: Article

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