Biomedical and Translational Informatics Laboratory

About Lab Research

The mission of the Ritchie Lab is to improve our understanding of the underlying genetic architecture of common diseases such as cancer, diabetes, cardiovascular disease, and pharmacogenomic traits among others. The approaches we explore will involve the development and application of new statistical and computational methods with a focus on the detection of gene-gene interactions, gene-environment interactions, and network and/or pathway effects associated with human disease.  Systems Genomics approaches, which involve the integration of multiple types of ‘omics data, is also a driving focus of the laboratory.  These meta-dimensional approaches hold the promise of providing a more comprehensive view of genetic and genomic information. 

All of these tools and methodologies that the Ritchie Lab develops focus on Big Data applications and emphasize improvements in visual analytics as we embrace the new horizons of genomic information.

Latest Software Releases

  • Biofilter 2.4.3:  The latest update for Biofilter is now available.  New in this version:
    • updated from Python2 to Python3
    • updated LOKI loaders
    • fixed a bug in removing redundant results
  • BioBin 2.3.0:  The latest update for BioBin is now available.  New in this version:
    • Improved genomic build detection and handling
    • Improved output formatting
    • New runtime options to better control the processing of sample, phenotype and VCF files

Recent Publications

  • Tong, B. et al. (2024). Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14349. Springer, Cham. https://doi.org/10.1007/978-3-031-45676-3_15
  • Compher, C. W., Quinn, R., Haslam, R., Bader, E., Weaver, J., Dudek, S., Ritchie, M. D., Lewis, J. D., & Wu, G. D. (2024). Penn Healthy Diet survey: pilot validation and scoring. The British Journal of Nutrition., 131(1), 156–162. https://doi.org/10.1017/S0007114523001642
  • Li, R., Benz, L., Duan, R., Denny, J. C., Hakonarson, H., Mosley, J. D., Smoller, J. W., Wei, W.-Q., Ritchie, M. D., Moore, J. H., & Chen, Y. (2024). MixWAS: An efficient distributed algorithm for mixed-outcomes genome-wide association studies. Medrxiv. https://doi.org/10.1101/2024.01.09.24301073
  • Verma, S. S., Gudiseva, H. V., Chavali, V. R. M., Salowe, R. J., Bradford, Y., Guare, L., Lucas, A., Collins, D. W., Vrathasha, V., Nair, R. M., Rathi, S., Zhao, B., He, J., Lee, R., Zenebe-Gete, S., Bowman, A. S., McHugh, C. P., Zody, M. C., Pistilli, M., … O’Brien, J. M. (2024). A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma. Cell, 187(2). https://doi.org/10.1016/j.cell.2023.12.006
  • Chae, A., Yao, M. S., Sagreiya, H., Goldberg, A. D., Chatterjee, N., MacLean, M. T., Duda, J., Elahi, A., Borthakur, A., Ritchie, M. D., Rader, D., Kahn, C. E., Witschey, W. R., & Gee, J. C. (2024). Strategies for implementing machine learning algorithms in the clinical practice of Radiology. Radiology, 310(1). https://doi.org/10.1148/radiol.223170