Primers_TiMet_AnnaFlis2013 (from BioDare)

RNA LD to Both LL and DD (from BioDare)

List of samples used in the assay (extracted from original BioDare metadata and converted to csv)

Original BioDare metadata, converted to json format

Readme file

An example template for screen published in IDR (idr0002, screenA).

Access Level: Published

https://idr.openmicroscopy.org/search/?key=name&value=idr0002&resource=container&operator=equals

300915_Ham2_GA_p97_experiment (from BioDare)

metamorph_output_abridged.2 (from BioDare)

List of samples used in the assay (extracted from original BioDare metadata and converted to csv)

Excel file enabling computation of transformed reference chemical potentials. This is an excel file with two sheets. The first and most important of these is a computation facility calculating the transformed chemical potentials of more than 360 compounds of biochemical interest and 1200 forms thereof. This sheet uses the second sheet as the work horse of the computations. The magnitudes of various transformed reference potentials are listed in the file https://fairdomhub.org/data_files/9798, ...

12 variously transformed reference potentials for 320 compounds of biochemical interest in unit kJ/mol.

Approximately 1360 of each type of transform are listed in columns 3 - 14. The number 1360 > 320 because a chemical compound often has multiple entries. This reflects that there are multiple data sources, phases, protonation states or Mg complexation states for that compound.

Column 2 contains the familiar reference (standard at the physical chemical reference state) physical chemical ...

AT0068 (from BioDare)

AT0039BRASS_Derandomised (from BioDare)

Plt15&16-TOC-200710-Prog.1 (from BioDare)

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.

Cell-type-specific marker genes were identified from the KPMP-derived transcriptomic dataset. For each cell type, a protein-protein interaction network was generated using STRING interactions among the identified genes. Network topology was analyzed using NetworkX, and multiple centrality metrics were computed to characterize gene importance and identify potential hub genes within each cellular context.