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Updated: Jul 10, 2026
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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
Published on: December 1, 2020
An interactive visualization-based approach for high throughput screening information management in drug discovery.
Tammy Pui Shan Chan1, Preeti Malik, Rahul Singh
1Dept. of Comput. Sci., San Francisco State Univ., CA 94132, USA.
Summary
This study introduces an intuitive system for managing high-throughput screening (HTS) assay data. It helps researchers easily find crucial patterns in complex biological data without needing advanced data-mining skills.
Area of Science:
- Bioinformatics
- Drug Discovery
- Data Science
Background:
- High-throughput screening (HTS) generates vast biological data, posing challenges for analysis.
- Life science researchers often lack expertise in complex data-mining algorithms.
- Information patterns can be lost in large HTS datasets.
Purpose of the Study:
- To provide an intuitive environment for storing and interacting with large HTS assay datasets.
- To enable researchers to easily discover complex information patterns.
- To address the limitations of current data assimilation and mining techniques.
Main Methods:
- Development of a user-friendly data interaction and visualization environment.
- Support for heterogeneous data modalities including chemical structures and assay formats.
- Integration of case studies and experimental validation.
Main Results:
- Demonstrated ease of use for researchers.
- Successful identification of complex patterns within HTS data.
- Effective handling of diverse data types common in drug discovery.
Conclusions:
- The proposed system enhances the usability of HTS data.
- It empowers researchers to extract valuable insights from complex datasets.
- Facilitates efficient drug discovery through improved data analysis.
