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h3k4me3 signal at a set of tissue-specific genes defined in the human protein atlas (hpa) database  (Human Protein Atlas)

 
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    Human Protein Atlas h3k4me3 signal at a set of tissue-specific genes defined in the human protein atlas (hpa) database
    H3k4me3 Signal At A Set Of Tissue Specific Genes Defined In The Human Protein Atlas (Hpa) Database, supplied by Human Protein Atlas, 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/tissue-specific+database/promoter+h3k4me3/pm37865722-252-44-51
    Average 90 stars, based on 1 article reviews
    h3k4me3 signal at a set of tissue-specific genes defined in the human protein atlas (hpa) database - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Isolation:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Clinical Proteomics:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Immunoprecipitation:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Next-Generation Sequencing:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Genome Wide:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Activity Assay:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    DNA Methylation Assay:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Generated:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Methylated DNA Immunoprecipitation:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Gene Expression:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Expressing:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Diagnostic Assay:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Marker:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Comparison:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Immunohistochemistry:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Control:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).

    Binding Assay:

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Cancer classification based on promoter signal Logistic regression with l2-norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA)24 using scikit-learn25.

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping
    Article Snippet: Logistic regression with l 2 -norm regularization was used to train biologically grounded and robust classifiers based on promoter H3K4me3 at lineage-enriched genes from the Human Protein Atlas (HPA) using scikit-learn .

    Article Title: Liquid biopsy epigenomic profiling for cancer subtyping.
    Article Snippet: Nature Medicine Brief Communication https://doi.org/10.1038/s41591-023-02605-z Extended Data Fig. 3 | Classification of cancer plasma based on H3K4me3 cfChIP-seq profiles. (a) Receiver operating characteristic (ROC) curves for logistic regression-based classification of cancer plasma vs. healthy plasma, using as features the promoter H3K4me3 signal at a set of tissue-specific genes defined in the Human Protein Atlas (HPA) database24 (Methods).



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