Availability of multi-level omics data such as genome, transcriptome, proteome and phosphatome data enables system-level analyses for the prediction and characterization of selective drug response on target diseases.
Integration of multi-level omics data with chemical or siRNA screening data on diverse biological samples is accelerating discovery studies on clinically relevant drug applications and their mode of actions.
For this purpose, we have constructed a smart screening platform combining technologies on computer-oriented big data mining and experimental high content screening for last several years.
▶ 1. Pan-omics data mining and screening
Method development and optimization for big data analysis
Generate diverse patterns and hypotheses describing the association between varied drug
response and molecular signatures such as mutation, gene or protein markers.
Validation of target molecules and samples for the optimization of high content siRNA or
chemical library screening
▶ 2. High throughput siRNA and chemical library screening
Image and cell-based assay development for high content screening
Automation and standardization of high throughput screening
System-level interpretation of siRNA library screening
▶ 3. Molecular modeling and drug design
Application of machine learning algorithms for cell-based SAR studies