Our Proprietary Predictive Models
Breast Cancer Survival Classifier (BCSC)
Predicts a breast cancer patient survival outcome (yes/no) based on a selected combination of gene expression levels of the tumour biopsy. The model was generated and validated by ML using O2Pmgen tool using a dataset of 239 TCGA Transcriptomes from the Human Protein Atlas database (BRCA).
Lung Cancer Survival Classifier (LCSC)
Predicts a lung cancer patient survival outcome (yes/no) based on a selected combination of gene expression levels of the tumour biopsy. The model was generated and validated by ML using O2Pmgen tool using a dataset of 325 TCGA Transcriptomes from the Human Protein Atlas database (LUSC, LUAD).
Renal Cancer Survival Classifier (RCSC)
Predicts a renal cancer patient survival outcome (yes/no) based on a selected combination of gene expression levels of the tumour biopsy. The model was generated and validated by ML using O2Pmgen tool using a dataset of 310 TCGA Transcriptomes from the Human Protein Atlas database (KICH KIRC KIRP).
Covid 19 Transmission Dynamics (Cov19TD)
Predicts the evolution dynamics of the COVID-19 infection over time in a given population (city). Enables estimating the number of infected cases and the peak of infection given a particular initial infection stage and mitigation strength. The model is based on 2 ordinary differential equations (ODE) with parameters taken from literature and calibrated using ML with real data collected from the COVID-19 infection.
EMT Regulatory Network (EMTRN)
A regulatory network model that predicts the Epithelial-to-Mesenchymal Transition based on 10 microenvironment inputs and the mutational impact on the regulatory network. The model is based on the logical framework that considers 51 regulatory components and 134 regulatory interactions, accounting for TGF, Integrin, Wnt, AKT, MAPK, HIF1, Notch, and Hippo signalling.
Empowering Biotech and Biomedical Research instituitions with state-of-the-art predictive modelling and bioinformatics solutions.