The Ning Lab is conducting research on Artificial Intelligence (AI), Data Mining, Machine Learning and Big Data Analytics with applications in drug discovery, Medical Informatics and Health Informatics. The research in the Lab is highly data-driven, computation-oriented and primarily focused on methodology development. The Lab develops new and better methods/algorithms/models to solve emerging and critical problems in healthcare and medicine domains. The new methodologies being developed at the Lab are also sufficiently generalizable to be applied to the problems from other domains (e.g., e-Commerce, social networks, system monitoring and diagnosis) that share similar characteristics as the targeted problems in healthcare and medicine domains. The typical techniques that the Lab develops include deep learning, information retrieval, learning to rank, recommendation, classification, among others, and are applied to problems such as drug property prediction, compound prioritization, drug selection, pharmacovigilance data mining, electronic medical record analysis, etc.
The research at Ning Lab is currently funded by NSF, NLM, NIMH, NIAMS, NIGMS, NEC Labs America, and Regenstrief Institute.
- Computational Methods to Explore Big Bioassay Data for Better Compound Prioritization
- Mining Drug-Drug Interaction Induced Adverse Effects from Health Record Databases
- Enhancing information retrieval in electronic health records through collaborative filtering
- Recommendation Algorithms for E-Commerce and Education