The overall goals of Cancer Genetics research at Michigan State University are to use cutting edge technology, big data, and computational approaches to identify genes and signaling pathways important in cancer.
Research Topics
- Computational analysis of gene expression data (Andrechek)
- Genes and signaling pathways important in the female reproductive system (Chandler)
- Leverage big data and artificial intelligence for cancer therapy (Chen)
- Integrate pedigrees, genomics and other omics in cancer (de los Campos)
- Epigenetic regulation and gene expression of cancer stem cells (He)
- Multi-protein complex function in gene transcription (Henry)
- Integrated cancer genomics and tumor syndromes (MacKeigan)
- Genomic landscape of complex tumor syndromes (Martin)
- Experimental and computational approaches to cancer (Mias)
- Computational tools and protein modeling to study cancer metastasis (Prokop)
- Cancer genetics in diagnostics (Thaiwong)
- Predictive genomic-based models in cancer treatment response and metastasis (Vasquez)
- Bioinformatics, machine learning, and gene regulation in cancer (Wang)
- Comparative molecular genetics, canine genome mapping, and cancer genetics (Yuzbasiyan-Gurkan)