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    v0.1 cell line networks
    v0.2 aggregate tissue networks
    v0.3 TF enrichement tool
    v0.4 single-sample tissue networks
    v0.5 CLUEreg tool
    v0.6 colon cancer networks
    v0.7 drug networks
    v0.7.1 API v1
    v0.8 liver, cervix, and breast cancer networks
    v0.9 miRNA tissue networks
    v0.9.1 bootstrap 5
    v1.0 glioblastoma networks
    v1.0.1 drug combinations
    v1.1 Cell line networks
    v1.2 User upload network
    v1.2.1 Gene targeting table
    v1.2.2 DRAGON miRNA network
    v1.3 Network comparison
    v1.3.1 Guided tours
    v1.4.0 CCLE multi-omic map
    v1.5.0 EGRET networks
    v1.6.0 BONOBO networks
    v1.7.0 tcga-data-nf networks

    Kidney cortex networks

    Data sets 1 Networks Aggregate 3

    To get the full list of phenotypic variables, please check the menu below.

    Tool description

    PANDA reconstructs a gene regulatory network using TF PPI, TF DNA binding motif as regulation prior, and gene expression samples. PUMA reconstructs a gene regulatory network with miRNA as regulators using gene expression samples and miRNA predicted targets by miRanda or TargetScan as regulation priors. The Regulator-regulator interaction matrix is set to the identity matrix in the algorithm. Check the phenotypic information for the clinical variables of the samples that were used to reconstruct the network.

    Publication
    Please check the reference Sonawane et al. (2017) at the following link.
    Publication
    Please check the reference Kuijjer et al. (2020) at the following link.
    Publication
    Please check the reference Kuijjer et al. (2020) at the following link.
    # Tissue Tool netZoo release Network
    PPI
    Regulation prior Expression
    Regulator
    nReg
    Genes Samples Precision
    Reference
    1 Kidney cortex PANDA netZooM 0.1
    AdjCodeVis
    Motif TF 644 30243 36 D
    2 Kidney cortex PUMA netZooM 0.3
    AdjCodeVis
    - miRanda miRNA 643 16161 36 D
    3 Kidney cortex PUMA netZooM 0.3
    AdjCodeVis
    - TargetScan miRNA 643 16161 36 D

    Variable description

    PANDA-LIONESS networks are single-sample networks generated by first estimating an aggregate PANDA network then deriving patient-specific LIONESS networks. This is a description of the primary clinical variables of the gene expression samples that were used to generate the gene regulatory network.

    Sample Subject Gender
    Age DTH-HRDY
    SMAT-SSCR
    SMRIN
    SMTS
    SMUBRID
    Isch. time
    Time point
    GTEX-11GS4-2326-SM-5A5KS GTEX-11GS4 1 60-69 2 2 6.1 Kidney 1225 910 Actual Death
    GTEX-11OF3-1326-SM-5N9FJ GTEX-11OF3 1 60-69 2 2 5.7 Kidney 1225 800 Presumed Death
    GTEX-11PRG-2226-SM-5GU5R GTEX-11PRG 1 50-59 2 3 6 Kidney 1225 931 Actual Death
    GTEX-11TTK-1926-SM-5PNW8 GTEX-11TTK 2 60-69 4 2 6.2 Kidney 1225 1154 Actual Death
    GTEX-12696-0926-SM-5FQTV GTEX-12696 1 60-69 3 1 7.8 Kidney 1225 521 Actual Death
    GTEX-12WSG-0826-SM-5EQ5A GTEX-12WSG 2 50-59 0 2 7.3 Kidney 1225 341 Actual Death
    GTEX-13112-2126-SM-5GCO4 GTEX-13112 1 50-59 2 2 6.2 Kidney 1225 1261 Actual Death
    GTEX-1399S-0526-SM-5IJG8 GTEX-1399S 2 30-39 0 1 5.9 Kidney 1225 75 Actual Death
    GTEX-13NYB-1726-SM-5N9G2 GTEX-13NYB 1 40-49 2 2 5.7 Kidney 1225 1056 Presumed Death
    GTEX-13O1R-2526-SM-5N9FW GTEX-13O1R 1 60-69 2 2 5.7 Kidney 1225 864 Actual Death
    GTEX-13OVI-1126-SM-5KLZF GTEX-13OVI 2 60-69 0 1 8.8 Kidney 1225 177 Actual Death
    GTEX-13OVL-1826-SM-5KLZR GTEX-13OVL 1 50-59 2 2 6 Kidney 1225 958 Actual Death
    GTEX-13OW6-1826-SM-5N9F9 GTEX-13OW6 1 50-59 2 1 5.7 Kidney 1225 629 Actual Death
    GTEX-13RTJ-2226-SM-5S2Q1 GTEX-13RTJ 1 60-69 4 3 5.9 Kidney 1225 922 Actual Death
    GTEX-145MN-0326-SM-5QGQI GTEX-145MN 1 30-39 0 1 7.5 Kidney 1225 112 Actual Death
    GTEX-147F4-2626-SM-5Q5CS GTEX-147F4 1 50-59 2 2 6.8 Kidney 1225 1311 Actual Death
    GTEX-1497J-0826-SM-5NQAJ GTEX-1497J 1 60-69 0 1 6.3 Kidney 1225 240 Actual Death
    GTEX-N7MS-1626-SM-3LK5F GTEX-N7MS 1 60-69 2 2 6.8 Kidney 1225 1250 Actual Death
    GTEX-NPJ8-2226-SM-3TW8D GTEX-NPJ8 1 40-49 4 1 7.2 Kidney 1225 342 Presumed Death
    GTEX-P4QS-1126-SM-3NMD5 GTEX-P4QS 1 60-69 0 1 6.4 Kidney 1225 157 Actual Death
    GTEX-QDVN-1626-SM-48TZC GTEX-QDVN 1 50-59 0 2 7.3 Kidney 1225 227 Actual Death
    GTEX-QLQW-1626-SM-4R1K1 GTEX-QLQW 1 30-39 0 1 8.4 Kidney 1225 812 Actual Death
    GTEX-RN64-1626-SM-48FD7 GTEX-RN64 1 50-59 1 2 6.6 Kidney 1225 780 Actual Death
    GTEX-T5JC-1526-SM-4DM68 GTEX-T5JC 1 20-29 2 2 5.9 Kidney 1225 723 Actual Death
    GTEX-UPIC-1126-SM-4IHLO GTEX-UPIC 2 20-29 0 1 7.4 Kidney 1225 236 Actual Death
    GTEX-WI4N-2026-SM-4OOS7 GTEX-WI4N 2 40-49 0 2 5.9 Kidney 1225 751 Actual Death
    GTEX-XPVG-0526-SM-4B65N GTEX-XPVG 1 50-59 0 1 7.9 Kidney 1225 86 Actual Death
    GTEX-Y5V6-2026-SM-5IFHO GTEX-Y5V6 1 60-69 0 1 6.5 Kidney 1225 191 Actual Death
    GTEX-ZC5H-1726-SM-5HL7X GTEX-ZC5H 2 40-49 0 2 6.1 Kidney 1225 288 Actual Death
    GTEX-ZDXO-0226-SM-4WKH7 GTEX-ZDXO 1 60-69 2 2 6.2 Kidney 1225 1167 Actual Death
    GTEX-ZE9C-1426-SM-4WKGM GTEX-ZE9C 1 60-69 2 2 6.1 Kidney 1225 650 Actual Death
    GTEX-ZLFU-0926-SM-5P9F8 GTEX-ZLFU 1 40-49 0 1 6.4 Kidney 1225 128 Actual Death
    GTEX-ZVZP-0926-SM-5GIDB GTEX-ZVZP 1 50-59 0 2 7.8 Kidney 1225 90 Actual Death
    GTEX-ZYFD-1526-SM-5NQ7T GTEX-ZYFD 1 50-59 3 2 5.8 Kidney 1225 629 Actual Death
    GTEX-ZYFG-1626-SM-5GZYY GTEX-ZYFG 2 60-69 0 1 8 Kidney 1225 106 Actual Death
    GTEX-ZYT6-2226-SM-5GIC9 GTEX-ZYT6 1 30-39 1 3 6.8 Kidney 1225 1430 Actual Death
    Sample Subject Gender Age DTH-HRDY SMAT-SSCR SMRIN SMTS SMUBRID SMTSISCH SMTSTPTREF
    Contact

    Department of Biostatistics
    Harvard T.H. Chan School of Public Health

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