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

05-02-2024
Lusheng, Mengtao and Hanyu respectively present posters on cancer disparities, antimicrobial peptides, and B-ALL subtype identification for the Symposium on ML/AI Applications in Biology and Medicine.

05-02-2024
Shibiao is invited to give a featured talk for the Symposium on ML/AI Applications in Biology and Medicine.

04-30-2024
An article “Artificial intelligence for omics data analysis" co-first-authored by Shibiao is accepted for publication by the journal BMC Methods. Congratulations!

04-26-2024
A research article preprint “SAMP: Identifying Antimicrobial Peptides by an Ensemble Learning Model Based on Proportionalized Split Amino Acid Composition” is online at bioRxiv. The link is here. Congratulations to Junxi, Mengtao and Andy!

04-12-2024
Shibiao is invited to serve as an Academic Editor for International Journal of Microbiology (IF 3.4).

04-09-2024
Lusheng presents two posters: "RanBALL: Identifying B-cell acute lymphoblastic leukemia subtypes based on an ensemble random projection model" and "Reducing health disparities for prostate adenocarcinoma by integrating multi-omics data via a multi-modal transfer learning approach" at AACR Annual Meeting 2024.

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