Computational biomedicine is the application of various computational techniques to analyse, understand, and organise the information associated with biomolecules in relation to the progression and onset of complex diseases, and it includes a wide range of subject areas ranging from structural biology, genomics, and proteomics to in vivo and in homo studies. The main starting point for data-based models of cancer is nowadays the Pan Cancer Atlas from The Cancer Genome Atlas (TCGA/GDC) database, providing a unified, comprehensive, in-depth, and interconnected understanding of how, where, and why tumours arise in human. However, public databases present an extremely huge amount of data and information that require a host of informatic tools to be accessed, exploited, and analysed. Moreover, non-specialists might lack the knowledge to find the appropriate method and how to apply it for a specific research question. Additionally, data need to be downloaded and pre-processed in diverse ways depending on the analytical method(s) of choice to enable downstream analyses. To overcome these problems and enable a wider access to these valuable sources of data by the research community, an open-source platform is being developed by our research team. This platform offers the opportunity for the researchers with no coding skills to retrieve and assemble data from TCGA (The Cancer Genome Atlas) and GDC (Genomic Data Commons) data hubs using a web browser and, subsequently, explore and analyse them in a responsive and semi-guided way that helps the researcher to make sense of data and results. As a result, our environment provides a network-based visualization of gene expression, epigenetic and/or proteomic data in the context of clinical risk and tumour characteristics.

Computational biomedicine is the application of various computational techniques to analyse, understand, and organise the information associated with biomolecules in relation to the progression and onset of complex diseases, and it includes a wide range of subject areas ranging from structural biology, genomics, and proteomics to in vivo and in homo studies. The main starting point for data-based models of cancer is nowadays the Pan Cancer Atlas from The Cancer Genome Atlas (TCGA/GDC) database, providing a unified, comprehensive, in-depth, and interconnected understanding of how, where, and why tumours arise in human. However, public databases present an extremely huge amount of data and information that require a host of informatic tools to be accessed, exploited, and analysed. Moreover, non-specialists might lack the knowledge to find the appropriate method and how to apply it for a specific research question. Additionally, data need to be downloaded and pre-processed in diverse ways depending on the analytical method(s) of choice to enable downstream analyses. To overcome these problems and enable a wider access to these valuable sources of data by the research community, an open-source platform is being developed by our research team. This platform offers the opportunity for the researchers with no coding skills to retrieve and assemble data from TCGA (The Cancer Genome Atlas) and GDC (Genomic Data Commons) data hubs using a web browser and, subsequently, explore and analyse them in a responsive and semi-guided way that helps the researcher to make sense of data and results. As a result, our environment provides a network-based visualization of gene expression, epigenetic and/or proteomic data in the context of clinical risk and tumour characteristics.