The flood of big data represents immense opportunity for making discoveries. Data scientists exploit powerful new approaches by combining aspects of computer science, statistics, applied mathematics, and visualization so that they can translate the vast amounts of data into new insights and new knowledge in various fields, including medicine, social science, engineering, marketing, business, public affair and so on.
Network Biology & Network Medicine
Linear connections between genotype and phenotype had been expected to underlie biological system. But the reality is that the most genotype-phenotype relationships arise from a much higher complex biological systems. To decipher the properties of biological systems, network formalism is on demand. Network science simplify complex biological systems into biological network in which nodes are metabolites and macromolecules such as protein and edges are physical, biochemical and functional interactions. By exploring and analyzing the biological networks, we will be able to understand how complex systems operate inside cells. Furthermore, this research can be extended to other domains, including social network analysis.
Computational Drug Development
The exponential growth of genomic and phenotypic data related to drug response is facilitating our understanding of drug action. However, there are lack of promising and efficient tools that properly utilize these data for discovering new indications from existing drugs. Advanced computational strategies are critical to attain a basis of precision medicine as well as to take a shortcut to find new drug indications.
Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning.