51902-Johnson, Travis

Travis S. Johnson, PhD

Assistant Research Professor of Biostatistics & Health Data Sciences

HITS 3000
Indianapolis, IN
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Key Publications

Johnson, T.S., Li, S., Franz, E., Huang, Z., Li, S.D., Campbell, M.J., Huang, K., Zhang, Y. (2019). PseudoFuN: Deriving functional potentials of pseudogenes from integrative relationships with genes and microRNAs across 32 cancers. Gigascience, 8(5), giz046.

Johnson, T.S., Wang, T., Huang, Z., Yu, C.Y., Wu, Y., Han, Y., Zhang, Y., Huang, K., Zhang, J. (2019). LAmbDA: Label Ambiguous Domain Adaptation Dataset Integration Reduces Batch Effects and Improves Subtype Detection. Bioinformatics, 35(22), 4696-4706.

Wang, T., Johnson, T. S., Shao, W., Lu, Z., Helm, B. R., Zhang, J., & Huang, K. (2019). BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes. Genome biology, 20(1), 1-15.

Abrams, Z. B., Johnson, T. S., Huang, K., Payne, P. R., & Coombes, K. (2019). A protocol to evaluate RNA sequencing normalization methods. BMC bioinformatics, 20(24), 1-7.

Johnson, T. S., Xiang, S., Helm, B. R., Abrams, Z. B., Neidecker, P., Machiraju, R., Zhang, Y., ... & Zhang, J. (2019). Spatial Cell Type Composition in Normal and Alzheimers Human Brains is Revealed Using Integrated Mouse and Human Single Cell RNA Sequencing. Scientific Reports.

Xiang, S.*, Johnson, T.*, Dong, T., Huang, Z., Cheng, M., Wang, T., ... & Zhang, J. (2020). Combinatorial analyses reveal that cellular composition changes have different impact on transcriptomic changes of cell type specific genes in Alzheimer’s Disease brains. Scientific Reports.

Titles & Appointments

  • Assistant Research Professor of Biostatistics & Health Data Sciences
  • Education
    2020 PhD The Ohio State University
    2016 MS The Ohio State University
    2015 AS Community College of the Air Force
    2014 BS Ohio University
  • Research
    My research is focused on methods development for omics data analysis. Specifically, I apply novel machine learning techniques to single cell RNA sequencing, bulk RNA sequencing, and multi-omic data. This algorithm development is focused on the subfield of machine learning called transfer learning so that knowledge can be imparted from one dataset to another. These projects focus on a wide range of diseases, including glioblastoma, alzheimer's disease, and multiple myeloma.

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