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Dr. Joseph Geraci, Ph.D.

Scientist/Bioinformatic Specialist
Ontario Cancer Biomarker Network
Adjunct Professor, Queen's University
Dept. of Pathology and Molecular Medicine

Joseph Geraci

BACKGROUND


As an undergraduate student, I studied neuroscience, physics, computer science and mathematics. I have a MSc in pure mathematics from the University of Toronto and a PhD in mathematical physics from the University of Toronto/University of Southern California, where I used graph theoretic methods to build a bridge between quantum information theory and classical statistical mechanics. I immediately jumped into a postdoc in oncology/bioinformatics at the Ontario Cancer Institute/Princess Margaret Hospital under the supervision of Igor Jurisica and Geoffrey Liu (links below). I studied the EGFR pathway from a protein interaction perspective coupled with a focus on single nucleotide polymorphisms. Link: EGFR pathway and SNPs

In addition to this, I began understanding and creating algorithms for the analysis of molecular networks. Link: Network Algorithms

After completing my postdoc, I went to work for the Ontario Cancer Biomarker Network (link: http://www.ocbn.ca/) where I have been developing novel technologies for the analysis of protein interaction networks and MRIs in addition to novel machine learning algorithms. I created a paradigm for clustering algorithms based on discrete dynamical systems and while funded by NSERC, created an algorithm named Butterfly which we are using for the discovery of proteomic and genomic based biomarkers. In addition to this, Butterfly is being used for the bottom-up discovery of subpopulations of patients within several diseases including several cancers and Alzheimer's disease. This approach allows us to begin to better understand the precise molecular differences that exist between patients, even within the same classical definition of a disease.

I have a strong interest in advancing the graph theoretic methods being employed in the study of our molecular circuitry (protein, micro RNA, mRNA) in addition to the analysis of brain MRIs. I am actively involved in several projects including a functional MRI depression study and am currently applying centrality measures to patient data with algorithms developed in-house. A major interest of mine is an ongoing project involving translating the vast amount of genetic and proteomic patient data, coupled with our current knowledge of our molecular circuitry, into a scoring scheme that can reveal potential new drug targets. This involves a marriage between the aforementioned Butterfly algorithm and several graph theoretic algorithms currently under development.

  • Igor's Link: http://www.cs.toronto.edu/~juris/
  • Geoffrey's link: http://medbio.utoronto.ca/faculty/liu_geoff.html

    Link to thesis:

  • https://tspace.library.utoronto.ca/bitstream/1807/16780/1/Geraci_Joseph_200811_PhD_thesis.pdf

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    CONTACT

    Tel: (416) 414-1789
    Email: joseph geraci (at) queensu.ca
    Office:


    PUBLICATIONS


  • Paper: http://iopscience.iop.org/1367-2630/12/7/075026
  • Paper: http://www.springerlink.com/content/q40r73p1xt6n77g6/
  • Paper: http://www.springerlink.com/content/w7t6588h54652w67/


    centralitymapGraph Theoretic Methods applied to an MRI

    Protein-Protein Interaction NetworkProtein-Protein Interaction Network

     

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    Page Created: 2011 Sept 01
    Page Last Updated: 2011 Sept 21