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summary receiver operating characteristic sroc curves  (STATA Corporation)


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    Structured Review

    STATA Corporation summary receiver operating characteristic sroc curves
    The ability <t>of</t> <t>APIP</t> to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) <t>SROC</t> curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot
    Summary Receiver Operating Characteristic Sroc Curves, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 99/100, based on 36112 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/summary receiver operating characteristic sroc curves/product/STATA Corporation
    Average 99 stars, based on 36112 article reviews
    summary receiver operating characteristic sroc curves - by Bioz Stars, 2026-05
    99/100 stars

    Images

    1) Product Images from "Clinical pathological significance and biological functions of APIP in hepatocellular carcinoma"

    Article Title: Clinical pathological significance and biological functions of APIP in hepatocellular carcinoma

    Journal: Cancer Cell International

    doi: 10.1186/s12935-025-04018-z

    The ability of APIP to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) SROC curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot
    Figure Legend Snippet: The ability of APIP to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) SROC curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot

    Techniques Used: Expressing



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    STATA Corporation summary receiver operating characteristic sroc curves
    The ability <t>of</t> <t>APIP</t> to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) <t>SROC</t> curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot
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    summary receiver operating characteristic sroc curves - by Bioz Stars, 2026-05
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    <t>Summary</t> <t>receiver</t> operator characteristic curve <t>(SROC)</t> of ML algorithms
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    STATA Corporation summary receiver operating characteristic sroc curve
    <t>Summary</t> <t>receiver</t> operator characteristic curve <t>(SROC)</t> of ML algorithms
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    Image Search Results


    The ability of APIP to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) SROC curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot

    Journal: Cancer Cell International

    Article Title: Clinical pathological significance and biological functions of APIP in hepatocellular carcinoma

    doi: 10.1186/s12935-025-04018-z

    Figure Lengend Snippet: The ability of APIP to distinguish HCC from normal liver tissue and its correlation with prognosis in HCC patients. ( a ) High expression of APIP in public database datasets. ( b ) Begg’s funnel plot. ( c ) SROC curve of APIP based on the TCGA cohort. ( d ) OS-Kaplan-Meier survival curve. ( e ) DFS-Kaplan-Meier survival curve. ( f ) Nomogram and calibration plot

    Article Snippet: To assess the clinical utility of APIP in HCC, standardized mean difference (SMD), summary receiver operating characteristic (SROC) curves, and pooled sensitivity and specificity analyses were conducted using Stata 12.0 software, and receiver operating characteristic (ROC) curves was conducted using R v3.6.1 packages“pROC”.

    Techniques: Expressing

    Summary receiver operator characteristic curve (SROC) of ML algorithms

    Journal: BMC Endocrine Disorders

    Article Title: Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis

    doi: 10.1186/s12902-025-01955-8

    Figure Lengend Snippet: Summary receiver operator characteristic curve (SROC) of ML algorithms

    Article Snippet: Fig. 7 Summary receiver operator characteristic curve (SROC) of ML algorithms

    Techniques: