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Research Interests
Research Interests
Understanding the mathematical representations of biological processes, relations, and functions in omics data and developing underlying analysis principles and mathematical theories.
Developing new systems biology models and AI frameworks to maximize the understanding of biological mechanisms in omics and multi-omics data.
Representation learning of high dimensional data, with a focus on linear/non-linear low rank and local low-rank representation of matrix and high order tensor.
Understanding biochemical variations in the microenvironment cancer and inflammatory diseases.
Developing explainable graphical/network models for biomedical data and transfer learning.
Study single cell and spatial multi-omics data to infer sample-wise/spatial dependent activity of transcriptional regulation, metabolism, and signaling pathways.
Biomarker prediction and development of personal wearable sensors and other smart health-related AI and biotechnologies.
Natural language processing-based mining of biological literature data.
Nutrient and food recommendations.
Our column brings you news and SOTA papers about GenAI, Computational Biology, AI4S, Quantum Computing, and many more!
Editor: Jiahe Yu, Yinuo Zhao, Haiqi Zhu, Pengtao Dang
Associated Editor: Chi Zhang, Sha Cao, Rosalie C. Sears, Yunlong Liu
News at a glance
News at a glance
Collaborative paper in statistical modeling of spatial transcriptomics was accepted by NeuRIPS 2026. 10/2026
Min Yang's paper "Quantum AI-Scientist: A Framework for Quantum Application Discovery in Biomedicine" was accepted by AAAI 2026 Fall Symposium. 10/2026
Changlin Wang received travel award from Montagna Symposium. 10/2026
Pengtao Dang's paper Forecasting Alzheimer’s Disease Progression with Deep Multimodal Learning: Integration of 3D MRI and Tabular Clinical Records via a Large Vision-Language Model was accepted by Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring. 09/2026
Chi chaired the Future Scientist in AI session at ICIBM 2026 in Buffalo, New York, supporting research opportunities for the next generation of scientists. 08/2026
Chi presented “Advancing AI approaches to study metabolic variations in diseases” at the 2026 ICSA China Conference in Shenzhen, China. 06/2026
BDRL presented two posters at the AACR Annual Meeting 2026 in San Diego, highlighting metabolic programs across tumor-infiltrating immune cells and a quantum-enabled resource for studying metabolic heterogeneity in the tumor microenvironment. 04/2026
Chi presented “Advancing AI approaches to study metabolic variations in diseases” at MCBIOS 2026 in Tampa, Florida. 03/2026
Xiao Wang presented “Inferring Transcriptional Dynamics from Spatial Transcriptomics with Physics-Informed Deep Learning” at ENAR 2026 in Indianapolis, Indiana. 03/2026
Metaknogic is online: knogic.org. Metaknogic is an AI co-scientist to interpret metabolic and nutritional mechanisms based on user input data. 03/2026
Our preprint “MetaKnogic-Alpha: A Hyper-Relational Knowledge Base for Grounded Metabolic Reasoning” is available on bioRxiv. This work integrates biological knowledge to support grounded reasoning about metabolism. 02/2026
Xiao presented the NOURISH project, “Nutritional Optimization Using Real-time Integrated Sensors and Health Digital Twins,” at FDT-BioTech FDA Day in Silver Spring, Maryland. 02/2026
BDRL Computational Biology Tools
BDRL Computational Biology Tools
Try our latest method to estimate metabolic flux or perform deconvolution via the Webservers
A webserver to estimate cell-/sample-wise metabolic fluxome by using scRNA-seq or general transcriptomics data
Predict the cell type PROPORTION and data specific cell type MARKER GENES
A semi-supervised approach for a robust identification of cell types and deconvolution of mouse transcriptomics data
More methods available in R/Python
More methods available in R/Python
Funding Source
Funding Source
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