Overview

The Integrated Systems Biology and AI — Tailored Pharmacology and Precision Medicine Lab of Hu Li, Ph.D., focuses on these areas of research:

  • Rigorous translational research in systems biology, systems pharmacology and individualized systems medicine, with a novel network approach to systems biology, pharmacology and medicine discoveries. This research leads to a better understanding of drug mode of action, drug response and therapeutic treatments for individual patients.
  • Development of novel network tools and integration of various large-scale biomedical omics data to unravel the molecular mechanisms and pathophysiological roots that underpin complex disease systems at the personalized network level.

Dr. Li and his research team are skilled in formulating novel systems biology concepts that help guide the development of innovative systems biology and artificial intelligence (AI) algorithms to unlock the underlying intricate interplay between genes that confer complex disease phenotypes. The active research areas of Dr. Li's lab include systems biology, systems pharmacology and individualized systems medicine.

The research team seeks to uncover meaningful biological information that explains the properties of big data from a new systems biology perspective to foster individualized disease diagnosis, drug discovery and precision medicine. The computational platforms that are developed, guided by a novel systems biology lens, can open new angles to better understand disease etiology, drug discovery, drug modes of action and treatment regimen design from systems aspects.

Over the past decades, Dr. Li's team has helped advance numerous new systems biology concepts and tools, including:

  • NetDecoder.
  • Personalized mutation evaluator (PERMUTOR).
  • Regulostat Inferelator (RSI).
  • Machine Learning-Assisted Network Inference (MALANI).
  • Gene Utility Model (GUM).
  • Phenotype mapping (P-Map).
  • Weight Engineering Artificial Neural Network Encoder (ANNE).
  • Manifold Medicine, manifold epigenetics, multiscale locked-state models (LoSMs).
  • Spatially Informed Artificial Intelligence (SPIN-AI).
  • Hypothesis-driven artificial intelligence (HD-AI).

Each of these advanced models and powerful tools can be used in broad disease types for novel discovery.