High Content Analysis (HCA) involves the extraction of multi-parametric data from cellular/sub-cellular images to quantify phenotypic alterations in cells. A High Content Screening (HCS) approach entails screening of thousands of compounds in high throughput fashion, generating millions/billions of image-based data. Deriving meaningful biological insights from HCA and HCS requires expertise in large-scale image analysis and cellular imaging. For more complex problems, techniques in Artificial Intelligence/Machine Learning (AI/ML) and multi-dimensional -omics data analytics are used to model and predict phenotypic behaviors associated with drug response.
Through years of involvement in research programs with the industry, other institutions and clinical advocates in Singapore and abroad, the Computational Phenomics Platform has acquired extensive experience in cellular image analysis for different models (cell-line, spheroids, organoids), AI/ML from large-scale image-based data, -omics data analytics (phenomics, transcriptomics, proteomics, genomics) and HTP screening data analytics.
CPP provides customized algorithms to convert high quality images to quantitative data for target validation, hit identification and confirmation.