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Multi-database mining and graph mining algorithms
 Current knowledge about genomics and proteomics
is expanding rapidly with many databases being created for special
purposes. This project will draw information from a collection of different
databases, to obtain maximal amount of knowledge of specific genes (or
proteins) with respect to their effect on specific processes. The
computational questions is to determine those genes which are optimal
targets for diagnosis or therapy, namely those genes which participate
in a large number of pathways and
processes (simpler problem) and for therapy, those genes which when
affected, block a certain pathway completely, with minimal effect on other
pathways. This is an NP hard problem which requires development of novel
computational methods. The work will
be in conjunction with leading researches in molecular biologists and
biochemistry to address some of the most current biological research
questions.
Requirements: This project is intended for MSc
Students in the Bioinformatics program which have also good knowledge in
several database mining languages such as Python, knowledge in graph
theoretic methods and the specific use of the Graph Boost Library.
Gene Dynamic Network Inference using Bayesian Methods
This project is intends to continue work done
by Omer Berkman on inference from a collection of “weak” Bayesian networks.
The inference is obtained on a regularitory network from a (long) time
series of Genes (or other markers) activations. In particular, the causal
regulation is sought, namely those markers which initiate the regulation of
other markers.(see related work in current
projects)
Similar causal effects are sought in brain imaging inference, as we are
using high resolution imaging (with EEG and MEG). See above.
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