EARO Computational Phenomics Platform

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 points. Deriving meaningful biological insights from HCA and HCS requires expertise in large-scale cellular imaging, as well as image and statistical analyses. For more complex problems, techniques in Artificial Intelligence/Machine Learning (AI/ML) and multi-dimensional -omics data analytics are essential to model and predict phenotypic behaviors associated with drug response, and for drug screening automation. 

Through years of involvement in research programs with the industry, other institutions and clinical advocates in Singapore and abroad, the Computational Phenomics Platform (CPP) has acquired extensive experience in supporting HCS/HCA data analytics and ChemoInformatics. The platform is adept at developing customized AI/ML and multi-omics data analytic solutions to support target validation, HTP screening, hit identification and validation in the drug discovery and development process.

Specifically, CPP will work with you to support various computational needs across your drug development projects, including cellular image analytics for different cellular models (cell-line, spheroids, organoids), AI/ML from large-scale image-based data, -omics data analytics (phenomics, transcriptomics, proteomics, genomics), HCS/HTP screening data analytics and structure-based modelling and screening.

• Identify drug inducing mitochondria fusion or fission (collab. Duke-NUS)

• Identify drug preventing neurite retraction ini ALS iPSCs (collab. IMCB A*STAR)

• Precision oncology approach for identification of anti-cancer drug for patients (collab. GIS A*STAR)

 

Key Capabilities

Our CPP team had worked on a broad range of projects including:

• Innovative analytics and visualisation for High Throughput Screening (HTS) and High Content Screening (HCS) assays.
• Image/Video Analytics of cellular assays, spheroids and organoids at single-cell resolution.
• Utilize Machine/Deep learning to predict complex biological outcomes from HTS and HCS data.
• Inter-omics data analytics (Transcriptomics, Proteomics, Genomics, Phenomics).
• Structure-based modelling and screening.

Key Expertise

• Deep content screening (DCS): AI-driven image-based phenotypic screening
• 4D high content screening of 3D tutor spheroids (Patent WO/2019/035766)
• Customization and automation of non image-based HTS/image-based HCS analysis and visualisation pipelines
• Integration of other -omics data with drug response phenotypic profiles; Multiplexed HTS/HCS
• Combinatorial in-silico and in-vitro screening