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<allpages gapcontinue="SUPREME:_A_Cancer_Subtype_Prediction_Methodology_by_Integrating_High-Dimensional_Biological_Datasets" />
<page pageid="667" ns="0" title="Reverse Engineering Gene Regulatory Networks by Integrating Multiple Types of High-Dimensional Biological Datasets">
<rev contentformat="text/x-wiki" contentmodel="wikitext" xml:space="preserve">Title: Reverse engineering gene regulatory networks by integrating multiple types of high-dimensional biological datasets
The underlying biological processes in living organisms could be summarized by a gene regulatory network (GRN). GRNs describe which gene regulates which gene in the
cell. Given that the DNA in our cells are same, the reason that our hand tissue is different than an eye tissue is due the differences in the GRNs in those cells.
Changes in GRNs could also lead into abnormal biological stages such as cancer. Building accurate GRNs of organisms is a key step to better characterize the system
and pinpoint potential drivers of diseases.
With the advent of high dimensional biological datasets, various types of evidences between regulatory interactions between genes can be computed. To goal of this
project is to integrate various types of datasets to reverse engineer GRNs with high accuracy. Students will work with a team of PhD students and the faculty mentor
to build a computational tool that leverages various high-dimensional biological datasets to infer regulatory interactions between genes in various experimental
Students are expected to be proficient in programming. Experience in molecular biology, basic Linux commands and high performance computing is preferred, but not
Student learning objectives: After this project, students will
* Have a basic understanding of molecular biology and high-dimensional biological datasets.
* Be familiar with R or Python programming language and some bioinformatics libraries in those languages.
* Learn gather biological data from public repositories
* Build a computational pipeline that pre-processes and integrates high-dimensional biological datasets
* Be familiar with data visualization tools to analyze and visualize gene networks
* Learn methods to evaluate predictive models by computing true positive rate, false positive rate, precision, recall, ROC curves, etc.</rev>
<page pageid="5" ns="0" title="Risk Modeling for Pyroclastic Volcano Flows">
<rev contentformat="text/x-wiki" contentmodel="wikitext" xml:space="preserve">'''Mentor:''' [http://www.marquette.edu/mscs/facstaff-spiller.shtml Elaine Spiller]
'''Approach:''' Develop and evaluate simplistic physical approximations that could be used as a fast surrogate
for computationally-intensive mass flow simulations.
'''Summary:''' A major hazard of volcanic activity is mass flows - debris flows, block and ash flows and hot
pyroclastic flows. After lying dormant for more than 300 years, the Soufriere Hills Volcano on the island
of Montserrat began an eruptive phase in 1996. Hundreds of mass flows have occurred during the last 13
years, ranging in size from thousands of cubic meters of material to 200 million cubic meters of material.
Several of the largest of these flows have caused tremendous damage to population centers on the island,
to the extent that today, more than half the island has been evacuated. A challenge for volcanology is to
predict the hazard risk at specific sites due to mass flows. Together with a group of geologists, engineers,
mathematicians and statisticians, Mentor Spiller is developing tools to quantify the risk from pyroclastic and
block and ash flows. Students on this project will learn about and implement methods to simulate rare
events, learn about statistical emulators, and work on developing simplistic physical approximations that
could be used as a fast surrogate for mass flow simulations.</rev>