Department of Computer Science
BioMaPS Institute for Quantitative Biology
Rutgers University
The methods we develop advance the understanding of gene function by
analysis of complex, heterogeneous experimental data including data
from imaging, for example from in situ gene expression experiments. Computer vision methods combined with statistical models
help to find functional modules in Drosophila
development by their spatial
and temporal co-expression patterns. Novel tree-models elucidate
regulatory mechanisms in the development of the lymphoid
system.
We advance the theory of classical bioinformatics tools
such as Hidden Markov Models and mixture
models and apply them to novel data. Our focus is on
semi-supervised learning, that is learning from labeled and unlabeled
data as one way to fuse different sources of data, and flexible,
robust models with minimal number of parameters
(CSI), which nevertheless agree well with the biological reality.
The data created by next generation
sequencing platforms poses challenges for implementing computational pipelines, for devising appropriate methods
for analysis and for scaling up statistically advanced approaches, e.g. Bayesian methods. In collaboration with
groups at Rutgers we analyze NGS data and develop efficient algorithms for Bayesian approaches. With teaching
(S10: Bioinformatics for next-generation sequencing, F10: Introduction to Bioinformatics) and organisation of
DIMACS workshops on NGS in 2010 and 2011 we also help to build and educate a community here at Rutgers.
Computational thinking is becoming a core
requirement across disciplines. Teaching computational and algorithmic ideas can benefit greatly
from software tools. We develop animation systems for graph
algorithms and clustering
algorithms; CATBox
is a Springer textbook using Gato. Learners can concentrate on tackling
exciting bioinformatics problems with our Hidden Markov Model
library.
Find out about the people in the lab, our research areas, our publications, the software tools we develop and maintain or how to contact us.