Statistical Machine
Learning Group @ FIU
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Welcome
to the Homepage of Statistical Machine Learning Group (SMLG) at Biostatistics
Department, Robert Stempel College of Public Health & Social Work at
Florida International
University. SMLG is led by Dr. Changwon Yoo. We mainly analyze big and complex data
using Big Data
Analytics (BDA), e.g., statistical and machine learning methods. Projects
of SMLG ranges from clinical translational models to gene-gene and
gene-environmental interactions in chronic diseases using BDA. We are
currently supported by national and international grant funds from agencies
such as NIH and SNUH. ·
Selected research
projects*: o
Identifying Key Genes that cause Aggressive Brain Cancer.
Funded by NIGMS. PI: Changwon Yoo The major goal of this project is to
develop a statistical model to identify key genes that cause aggressive brain
cancer and further model the key genes causal interactions. o
Analysis of Clinical Parameters of Benign Prostatic Hyperplasia
using Causal Bayesian Network, Funded by Seoul National University
Hospital, Seoul, Korea. We propose to learn interactions among the genes and
environment factors. We will test the model using simulated data. Based on
the clinical electronic database of Benign Prostatic Hyperplasia patients, we
will develop a statistical model that provides better ways to measure and
understanding of Bladder Outlet Obstruction. * To access some links and complete
list of projects, please register in the SMLG Discussion Forum and/or SMLG Git
Lab. ·
Selected
publications: o
C. Yoo, V. Thorsson, G.F.
Cooper. Discovery of causal
relationships in a gene-regulation pathway from a mixture of experimental and
observational DNA microarray data. Pacific Symposium On
Biocomputing. p498-509, 2002 o
C. Yoo, E. Blitz. Local
Causal Discovery Algorithm using Causal Bayesian networks. Annals of the
NY Academy of Science, 1158, p93-101, 2009 o
C. Yoo, Bayesian
Method for Causal Discovery of Latent-Variable Models from a Mixture of
Experimental and Observational Data, Computational Statistics and Data
Analysis, 56, p2183-2205, 2012 o
C. Yoo, E. Brilz, Efficient and Scalable Bayesian
Statistical Method for Identifying Causal Relationships from Intervention
Studies, Advances and Applications in Statistics, 37(2), p95-122, 2013 Dr. Yoo’s early publications represents novel statistical
causal discovery methods. At that time most of the gene-gene interactions
were modeled with correlation not causation. In the new systems biology era,
the importance of causal model has been emphasizes by many scholars; e.g.,
Kitano (in his 2002 Science article)* references Dr. Yoo’s
causal analysis paper and states that the causal modeling that Dr. Yoo is
using is the future direction that systems biology researchers should follow.
Indeed, now the causal modeling has become more and more important in systems
biology research. * Kitano, H., Systems Biology: a
brief overview, Science, 2002 Mar 1;295(5560):1662-4. |
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Please
contact cyoo@fiu.edu if you have any
questions or comments about this website. Please go to SMLG
Discussion Forum to post any questions/comments about SMLG.