Welcome to Wan Lab@UNMC

Machine Learning and Bioinformatics (MLAB) Lab

The Wan Lab in the Department of Genetics, Cell Biology and Anatomy (GCBA) at University of Nebraska Medical Center (UNMC) is focusing on machine learning, bioinformatics, and computational biology, especially in single-cell analysis, multi-omics analysis, spatial transcriptomics, cancer research, intelligent healthcare, and precision medicine. To unravel the mechanisms of molecular biological systems in which enormous amounts of heterogeneous data are usually involved, bioinformatics and machine learning are perfect tools. Besides collaborating with scientists in cancer biology, metabolism, immunology, pathology and developmental biology, our laboratory is mainly to develop artificial intelligence, machine learning and/or data science-based methods to tackle essential biomedical problems in genomics, transcriptomics, epigenetics, proteomics, metabolomics, and interactomes as well as medical imaging data and electronic health records (EHR) data.

We are looking for passionate new PhD students, Postdocs, and Master students to join our team (more info) !

News

12-18-2025
A research article preprint “A Multi-Modal Transfer Learning Framework to Reduce Health Disparities in Prostate Adenocarcinoma” is online at bioRxiv. The link is here. Congratulations to Lusheng!

12-03-2025
A research article preprint “Mapping Structural and Molecular Spatiotemporal Dynamics of Macaque Brain during Development and Aging” collaborating with Dr. Jiaojian Wang's lab is available online at Research Square. The link is here. Congratulations to Xinchao as a co-first author!

11-30-2025
A research article titled “Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects by RaMBat” is accepted by the journal Molecular Oncology. Congratulations to Mengtao!

11-26-2025
Shibiao is invited to be a Grant Reviewer for the Natural Sciences and Engineering Research Council of Canada (NSERC).

11-19-2025
Shibiao is invited to be a Grant Reviewer for the NIH Special Emphasis Panel (SEP) in Biodata Management and Computational Modeling.

11-14-2025
Shibiao is invited to serve as a reviewer for ACM Computing Surveys (IF 28; ranked 1/147 in Computer Science Theory & Methods).

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