Data files

AT0072 (from BioDare)

AT0072BrassDerandomized (from BioDare)

AT0072 (from BioDare)

AT0072 (from BioDare)

STANDARD_DATAFILE.1 (from BioDare)

STANDARD_RAWDATAFILE (from BioDare)

AT0072.2 (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

AT0068 (from BioDare)

AT0039BRASS_Derandomised (from BioDare)

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

The raw data fror our study of optimal biological thermodynamics consist of the names, the phase, the physical chemical molar Gibbs energies of formation and the enthalpies of formation (both in kJ/mol; for Temperature 298.15 K and pressure 1 bar), reported in multiple Tables in the literature, as well as the atomic composition, the electric charge and the phase of the chemical compound. The latter were inferred by looking up the structure of each compound in the literature. These raw data were ...

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.

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.

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.