Supervised Multi-view Canonical Correlation Analysis (sMVCCA): Integrating histologic and proteomic features for predicting recurrent prostate cancer

IEEE Transactions on Medical Imaging

Volumn 34, Issue 1, 2015, Pages 284-297

  Lee, George ^a   Singanamalli, Asha ^a   Wang, Haibo ^a   Feldman, Michael D ^b   Master, Stephen R ^b   Shih, Natalie N C ^b   Spangler, Elaine ^b   Rebbeck, Timothy ^b   Tomaszewski, John E ^c   Madabhushi, Anant ^a  

^a Case Western Reserve University   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c UNIVERSITY AT BUFFALO   (United States)

Author keywords

Data fusion; digital pathology; dimensionality reduction; mass spectrometry; prostate cancer; proteomics

Indexed keywords

CLUSTERING ALGORITHMS; CORRELATION METHODS; DATA FUSION; DATA INTEGRATION; DATA MINING; DECISION TREES; DISEASES; FORECASTING; MASS SPECTROMETRY; MOLECULAR BIOLOGY; PROTEINS; UROLOGY;

CANONICAL CORRELATION ANALYSIS; DIGITAL PATHOLOGIES; DIMENSIONALITY REDUCTION; HIGH-DIMENSIONAL DATA STREAMS; PROSTATE CANCERS; PROTEOMICS; RECURRENT PROSTATE CANCERS; SPEARMAN'S RANK CORRELATION;

CLASSIFICATION (OF INFORMATION);

PROTEOME; TUMOR MARKER;

ARTICLE; BIOCHEMICAL RECURRENCE; BIOCHEMICAL RECURRENCE FREE SURVIVAL; CANCER CLASSIFICATION; CANCER GROWTH; CANCER RECURRENCE; CLINICAL ARTICLE; CLINICAL FEATURE; CORRELATION ANALYSIS; GLEASON SCORE; HISTOLOGY; HUMAN; MALE; MORPHOMETRICS; MULTI VIEW CANONICAL CORRELATION ANALYSIS; NONLINEAR SYSTEM; PREDICTION; PROSTATE BIOPSY; PROSTATE CANCER; PROSTATECTOMY; PROTEIN EXPRESSION; PROTEOMICS; QUANTITATIVE ANALYSIS; RANDOM FOREST; RECURRENCE RISK; RECURRENT DISEASE; RECURRENT PROSTATE CANCER; ALGORITHM; BIOLOGY; CHEMISTRY; CYTOCHEMISTRY; IMAGE PROCESSING; KAPLAN MEIER METHOD; METABOLISM; MORTALITY; PATHOLOGY; PROCEDURES; PROGNOSIS; PROSTATIC NEOPLASMS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; HISTOCYTOCHEMISTRY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; KAPLAN-MEIER ESTIMATE; MALE; PROGNOSIS; PROSTATIC NEOPLASMS; PROTEOME; PROTEOMICS; TUMOR MARKERS, BIOLOGICAL;

EID: 84937559607     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2014.2355175     Document Type: Article

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