Review



codelink whole human genome arrays  (Illumina Inc)


Bioz Verified Symbol Illumina Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    Illumina Inc codelink whole human genome arrays
    ( a ) Members of the network as a classifier of neonatal infection using leave-one-out cross-validation analysis using four independent machine-learning algorithms (red circle, random forest; green triangle, support vector machines; blue+, K nearest neighbour and black x, receiver operator characteristics). ( b ) Flow chart of biomarker classifier validation. Flow chart showing the samples used for classifier generation and the validation results. ( c ) Heat map showing hierarchical clustering of 18 infant samples (9 bacterially infected, 6 control, 3 virally infected) based on the 50 probes that were in common between the classifier gene set and the Affymetrix U219 platform. Hierarchical clustering was based on Euclidean distance. Control, blue; bacterial infected, red; viral infected, black. Classification of bacterial infection is indicated (0=non-infected, 1=infected). ( d ) Comparison of microarray-based classifier and expert assessment for classification of samples from patients with suspected infection. (Top) Comparison of expert assessment, CD69/FCGR1A and the 52-gene classifier on samples scored ‘high’ and ‘low’ likelihood of infection by expert assessment. (Middle) 52-gene classifier prediction and expert assessment of suspected sepsis cases (red=concordance of ‘infection’, blue=concordance of ‘control’, dark grey=discordance of microarray classifier and expert assessment). (Bottom left) Comparison of CD69/FCGR1A and 52-gene classifier of samples scored ‘medium’ likelihood of infection by expert assessment. Classification of bacterial infection is indicated (0=non-infected (pale blue), 1=infected (pink)). (Bottom right) Sensitivity, specificity and accuracy of CD69/FCGR1A and the 52-gene classifier against expert classification for suspected samples from d top. A heat map showing the 30 infant samples of suspected infection based on the 46 probes that were in common between the classifier gene set and the <t>CodeLink</t> platform is shown in . ( e ) Bar plots showing clinical criteria in suspected infection cases as judged by the 52-gene classifier and expert assessment. Days on antibiotics and neutrophil counts are shown for samples based on classification of control (pale blue) and bacterial infection (pink) showing median and standard error of the median. Days on antibiotics, classifier prediction: infected: n =15 (excluding 2 missing values); control: n =12 (excluding 1 missing value). Days on antibiotics, expert assessment: infected: n =5 (excluding 1 missing value); control: n =14 (excluding 1 missing value). Neutrophil count, classifier prediction: infected, n =17; control, n =13. Neutrophil count, expert assessment: infected: n =6; control: n =15.
    Codelink Whole Human Genome Arrays, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/codelink+whole+human+genome+arrays/codelink+whole+human+genome+arrays/pmc04143936-228-21-4
    Average 90 stars, based on 1 article reviews
    codelink whole human genome arrays - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "Identification of a human neonatal immune-metabolic network associated with bacterial infection"

    Article Title: Identification of a human neonatal immune-metabolic network associated with bacterial infection

    Journal: Nature Communications

    doi: 10.1038/ncomms5649

    ( a ) Members of the network as a classifier of neonatal infection using leave-one-out cross-validation analysis using four independent machine-learning algorithms (red circle, random forest; green triangle, support vector machines; blue+, K nearest neighbour and black x, receiver operator characteristics). ( b ) Flow chart of biomarker classifier validation. Flow chart showing the samples used for classifier generation and the validation results. ( c ) Heat map showing hierarchical clustering of 18 infant samples (9 bacterially infected, 6 control, 3 virally infected) based on the 50 probes that were in common between the classifier gene set and the Affymetrix U219 platform. Hierarchical clustering was based on Euclidean distance. Control, blue; bacterial infected, red; viral infected, black. Classification of bacterial infection is indicated (0=non-infected, 1=infected). ( d ) Comparison of microarray-based classifier and expert assessment for classification of samples from patients with suspected infection. (Top) Comparison of expert assessment, CD69/FCGR1A and the 52-gene classifier on samples scored ‘high’ and ‘low’ likelihood of infection by expert assessment. (Middle) 52-gene classifier prediction and expert assessment of suspected sepsis cases (red=concordance of ‘infection’, blue=concordance of ‘control’, dark grey=discordance of microarray classifier and expert assessment). (Bottom left) Comparison of CD69/FCGR1A and 52-gene classifier of samples scored ‘medium’ likelihood of infection by expert assessment. Classification of bacterial infection is indicated (0=non-infected (pale blue), 1=infected (pink)). (Bottom right) Sensitivity, specificity and accuracy of CD69/FCGR1A and the 52-gene classifier against expert classification for suspected samples from d top. A heat map showing the 30 infant samples of suspected infection based on the 46 probes that were in common between the classifier gene set and the CodeLink platform is shown in . ( e ) Bar plots showing clinical criteria in suspected infection cases as judged by the 52-gene classifier and expert assessment. Days on antibiotics and neutrophil counts are shown for samples based on classification of control (pale blue) and bacterial infection (pink) showing median and standard error of the median. Days on antibiotics, classifier prediction: infected: n =15 (excluding 2 missing values); control: n =12 (excluding 1 missing value). Days on antibiotics, expert assessment: infected: n =5 (excluding 1 missing value); control: n =14 (excluding 1 missing value). Neutrophil count, classifier prediction: infected, n =17; control, n =13. Neutrophil count, expert assessment: infected: n =6; control: n =15.
    Figure Legend Snippet: ( a ) Members of the network as a classifier of neonatal infection using leave-one-out cross-validation analysis using four independent machine-learning algorithms (red circle, random forest; green triangle, support vector machines; blue+, K nearest neighbour and black x, receiver operator characteristics). ( b ) Flow chart of biomarker classifier validation. Flow chart showing the samples used for classifier generation and the validation results. ( c ) Heat map showing hierarchical clustering of 18 infant samples (9 bacterially infected, 6 control, 3 virally infected) based on the 50 probes that were in common between the classifier gene set and the Affymetrix U219 platform. Hierarchical clustering was based on Euclidean distance. Control, blue; bacterial infected, red; viral infected, black. Classification of bacterial infection is indicated (0=non-infected, 1=infected). ( d ) Comparison of microarray-based classifier and expert assessment for classification of samples from patients with suspected infection. (Top) Comparison of expert assessment, CD69/FCGR1A and the 52-gene classifier on samples scored ‘high’ and ‘low’ likelihood of infection by expert assessment. (Middle) 52-gene classifier prediction and expert assessment of suspected sepsis cases (red=concordance of ‘infection’, blue=concordance of ‘control’, dark grey=discordance of microarray classifier and expert assessment). (Bottom left) Comparison of CD69/FCGR1A and 52-gene classifier of samples scored ‘medium’ likelihood of infection by expert assessment. Classification of bacterial infection is indicated (0=non-infected (pale blue), 1=infected (pink)). (Bottom right) Sensitivity, specificity and accuracy of CD69/FCGR1A and the 52-gene classifier against expert classification for suspected samples from d top. A heat map showing the 30 infant samples of suspected infection based on the 46 probes that were in common between the classifier gene set and the CodeLink platform is shown in . ( e ) Bar plots showing clinical criteria in suspected infection cases as judged by the 52-gene classifier and expert assessment. Days on antibiotics and neutrophil counts are shown for samples based on classification of control (pale blue) and bacterial infection (pink) showing median and standard error of the median. Days on antibiotics, classifier prediction: infected: n =15 (excluding 2 missing values); control: n =12 (excluding 1 missing value). Days on antibiotics, expert assessment: infected: n =5 (excluding 1 missing value); control: n =14 (excluding 1 missing value). Neutrophil count, classifier prediction: infected, n =17; control, n =13. Neutrophil count, expert assessment: infected: n =6; control: n =15.

    Techniques Used: Infection, Plasmid Preparation, Biomarker Assay, Microarray

    Related Articles

    Microarray:

    Article Title: Identification of a human neonatal immune-metabolic network associated with bacterial infection
    Article Snippet: .. In addition to the Illumina microarray analysis, we examined a subset of 42 of these samples (18 infected, 24 controls) using CodeLink Whole Human Genome arrays, referred to as ‘platform test set’. .. Subsequently, the classifier was applied to a further 26 new and independent samples (16 bacterially infected samples from 15 infants and 10 control samples), which were run on CodeLink (7 infected and 3 control), Affymetrix HG-U133 (two infected, three control) or Affymetrix Human Genome U219 (9 infected and 6 control) arrays.

    Infection:

    Article Title: Identification of a human neonatal immune-metabolic network associated with bacterial infection
    Article Snippet: .. In addition to the Illumina microarray analysis, we examined a subset of 42 of these samples (18 infected, 24 controls) using CodeLink Whole Human Genome arrays, referred to as ‘platform test set’. .. Subsequently, the classifier was applied to a further 26 new and independent samples (16 bacterially infected samples from 15 infants and 10 control samples), which were run on CodeLink (7 infected and 3 control), Affymetrix HG-U133 (two infected, three control) or Affymetrix Human Genome U219 (9 infected and 6 control) arrays.



    Similar Products

    codelink whole human genome arrays  (Illumina Inc)
    90
    Illumina Inc codelink whole human genome arrays
    ( a ) Members of the network as a classifier of neonatal infection using leave-one-out cross-validation analysis using four independent machine-learning algorithms (red circle, random forest; green triangle, support vector machines; blue+, K nearest neighbour and black x, receiver operator characteristics). ( b ) Flow chart of biomarker classifier validation. Flow chart showing the samples used for classifier generation and the validation results. ( c ) Heat map showing hierarchical clustering of 18 infant samples (9 bacterially infected, 6 control, 3 virally infected) based on the 50 probes that were in common between the classifier gene set and the Affymetrix U219 platform. Hierarchical clustering was based on Euclidean distance. Control, blue; bacterial infected, red; viral infected, black. Classification of bacterial infection is indicated (0=non-infected, 1=infected). ( d ) Comparison of microarray-based classifier and expert assessment for classification of samples from patients with suspected infection. (Top) Comparison of expert assessment, CD69/FCGR1A and the 52-gene classifier on samples scored ‘high’ and ‘low’ likelihood of infection by expert assessment. (Middle) 52-gene classifier prediction and expert assessment of suspected sepsis cases (red=concordance of ‘infection’, blue=concordance of ‘control’, dark grey=discordance of microarray classifier and expert assessment). (Bottom left) Comparison of CD69/FCGR1A and 52-gene classifier of samples scored ‘medium’ likelihood of infection by expert assessment. Classification of bacterial infection is indicated (0=non-infected (pale blue), 1=infected (pink)). (Bottom right) Sensitivity, specificity and accuracy of CD69/FCGR1A and the 52-gene classifier against expert classification for suspected samples from d top. A heat map showing the 30 infant samples of suspected infection based on the 46 probes that were in common between the classifier gene set and the <t>CodeLink</t> platform is shown in . ( e ) Bar plots showing clinical criteria in suspected infection cases as judged by the 52-gene classifier and expert assessment. Days on antibiotics and neutrophil counts are shown for samples based on classification of control (pale blue) and bacterial infection (pink) showing median and standard error of the median. Days on antibiotics, classifier prediction: infected: n =15 (excluding 2 missing values); control: n =12 (excluding 1 missing value). Days on antibiotics, expert assessment: infected: n =5 (excluding 1 missing value); control: n =14 (excluding 1 missing value). Neutrophil count, classifier prediction: infected, n =17; control, n =13. Neutrophil count, expert assessment: infected: n =6; control: n =15.
    Codelink Whole Human Genome Arrays, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/codelink+whole+human+genome+arrays/codelink+whole+human+genome+arrays/pmc04143936-228-21-4
    Average 90 stars, based on 1 article reviews
    codelink whole human genome arrays - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    ( a ) Members of the network as a classifier of neonatal infection using leave-one-out cross-validation analysis using four independent machine-learning algorithms (red circle, random forest; green triangle, support vector machines; blue+, K nearest neighbour and black x, receiver operator characteristics). ( b ) Flow chart of biomarker classifier validation. Flow chart showing the samples used for classifier generation and the validation results. ( c ) Heat map showing hierarchical clustering of 18 infant samples (9 bacterially infected, 6 control, 3 virally infected) based on the 50 probes that were in common between the classifier gene set and the Affymetrix U219 platform. Hierarchical clustering was based on Euclidean distance. Control, blue; bacterial infected, red; viral infected, black. Classification of bacterial infection is indicated (0=non-infected, 1=infected). ( d ) Comparison of microarray-based classifier and expert assessment for classification of samples from patients with suspected infection. (Top) Comparison of expert assessment, CD69/FCGR1A and the 52-gene classifier on samples scored ‘high’ and ‘low’ likelihood of infection by expert assessment. (Middle) 52-gene classifier prediction and expert assessment of suspected sepsis cases (red=concordance of ‘infection’, blue=concordance of ‘control’, dark grey=discordance of microarray classifier and expert assessment). (Bottom left) Comparison of CD69/FCGR1A and 52-gene classifier of samples scored ‘medium’ likelihood of infection by expert assessment. Classification of bacterial infection is indicated (0=non-infected (pale blue), 1=infected (pink)). (Bottom right) Sensitivity, specificity and accuracy of CD69/FCGR1A and the 52-gene classifier against expert classification for suspected samples from d top. A heat map showing the 30 infant samples of suspected infection based on the 46 probes that were in common between the classifier gene set and the CodeLink platform is shown in . ( e ) Bar plots showing clinical criteria in suspected infection cases as judged by the 52-gene classifier and expert assessment. Days on antibiotics and neutrophil counts are shown for samples based on classification of control (pale blue) and bacterial infection (pink) showing median and standard error of the median. Days on antibiotics, classifier prediction: infected: n =15 (excluding 2 missing values); control: n =12 (excluding 1 missing value). Days on antibiotics, expert assessment: infected: n =5 (excluding 1 missing value); control: n =14 (excluding 1 missing value). Neutrophil count, classifier prediction: infected, n =17; control, n =13. Neutrophil count, expert assessment: infected: n =6; control: n =15.

    Journal: Nature Communications

    Article Title: Identification of a human neonatal immune-metabolic network associated with bacterial infection

    doi: 10.1038/ncomms5649

    Figure Lengend Snippet: ( a ) Members of the network as a classifier of neonatal infection using leave-one-out cross-validation analysis using four independent machine-learning algorithms (red circle, random forest; green triangle, support vector machines; blue+, K nearest neighbour and black x, receiver operator characteristics). ( b ) Flow chart of biomarker classifier validation. Flow chart showing the samples used for classifier generation and the validation results. ( c ) Heat map showing hierarchical clustering of 18 infant samples (9 bacterially infected, 6 control, 3 virally infected) based on the 50 probes that were in common between the classifier gene set and the Affymetrix U219 platform. Hierarchical clustering was based on Euclidean distance. Control, blue; bacterial infected, red; viral infected, black. Classification of bacterial infection is indicated (0=non-infected, 1=infected). ( d ) Comparison of microarray-based classifier and expert assessment for classification of samples from patients with suspected infection. (Top) Comparison of expert assessment, CD69/FCGR1A and the 52-gene classifier on samples scored ‘high’ and ‘low’ likelihood of infection by expert assessment. (Middle) 52-gene classifier prediction and expert assessment of suspected sepsis cases (red=concordance of ‘infection’, blue=concordance of ‘control’, dark grey=discordance of microarray classifier and expert assessment). (Bottom left) Comparison of CD69/FCGR1A and 52-gene classifier of samples scored ‘medium’ likelihood of infection by expert assessment. Classification of bacterial infection is indicated (0=non-infected (pale blue), 1=infected (pink)). (Bottom right) Sensitivity, specificity and accuracy of CD69/FCGR1A and the 52-gene classifier against expert classification for suspected samples from d top. A heat map showing the 30 infant samples of suspected infection based on the 46 probes that were in common between the classifier gene set and the CodeLink platform is shown in . ( e ) Bar plots showing clinical criteria in suspected infection cases as judged by the 52-gene classifier and expert assessment. Days on antibiotics and neutrophil counts are shown for samples based on classification of control (pale blue) and bacterial infection (pink) showing median and standard error of the median. Days on antibiotics, classifier prediction: infected: n =15 (excluding 2 missing values); control: n =12 (excluding 1 missing value). Days on antibiotics, expert assessment: infected: n =5 (excluding 1 missing value); control: n =14 (excluding 1 missing value). Neutrophil count, classifier prediction: infected, n =17; control, n =13. Neutrophil count, expert assessment: infected: n =6; control: n =15.

    Article Snippet: In addition to the Illumina microarray analysis, we examined a subset of 42 of these samples (18 infected, 24 controls) using CodeLink Whole Human Genome arrays, referred to as ‘platform test set’.

    Techniques: Infection, Plasmid Preparation, Biomarker Assay, Microarray