Daniel Ruiz-Perez

School of Computing & Information Sciences

Lecture Information:
  • June 25, 2019
  • 2:00 PM
  • CASE 349

Speaker Bio

Daniel Ruiz-Perez is a Ph.D. candidate in the Bioinformatics Research Group (BioRG) at the School of Computing and Information Sciences. He holds a B.Sc. in Computer Science from the University of A Coruña, in Spain. There, he worked as a software engineer at a local company and is currently doing a data science summer internship at Assurant in Miami. He has published papers in important journals like BMC Microbiome and conferences like ICCABS. He was awarded multiple scholarships and awards including the Golden Key Research Grant and the Biomedical Research Initiative from FIU. He is also a member of professional and social fraternities and holds a black belt in Judo.


Understanding microbial communities beyond their mere composition involves identifying important microbial interactions, and can lead to the development of better drugs and treatments. More importantly, we hypothesize that novel analysis techniques are needed to analyze data from longitudinal studies. First, we developed a computational pipeline to infer microbial interactions and the impact of clinical factors from longitudinal data. The time series were interpolated using splines to deal with non-uniform sampling and missing values, then temporally aligned to the best reference time series to account for differential rates of change between the different subjects. Finally, a Dynamic Bayesian Network (DBN) model was created, allowing for the inference of potential microbial interactions. As a second project, we propose the extension of the work mentioned above to allow the inclusion of multiomics data; Temporal data from metagenomics, metabolomics, metatranscriptomics and clinical information will be integrated into the model. The DBN will be restricted to interactions consistent with the current metabolic framework. For the last project, we hypothesize that the use of non-negative matrix factorization (NMF) over data from longitudinal studies can shed light on the structure of the microbial community.