OCT 01, 2015 1:30 PM PDT

Data, Data, Everywhere - Using Complexity Analysis to find coherence in Cancer Big Data

Speaker
  • Director of Computational Sciences and Informatics program for Complex Adaptive Systems and Professor in the School of Life Sciences, Arizona State University
    BIOGRAPHY

Abstract

Multidimensional molecular characterization has led to a tsunami of cancer data. Precision Medicine assumes that new understanding and better interventions will flow from this “Big Data”. However, the cacophony of data has challenged many of the founding paradigms of cancer and when viewed through these simplifying lenses appears chaotic. Cancer has been observed to not only complicated, but also complex. Coherence emerges from the apparent disorder when the molecular cancer Big Data is examined through methods of complex adaptive systems. For example, complex phenotypes such as susceptibility to cancer and response to interventions have regular, reproducible patterns when the interaction between components is taken into account through molecular networks. Examination of molecular networks offers a means to capture gene-centric concepts and complex, emergent behavior. More specifically, in analysis of breast cancer and liver cancer data networks provide much stronger signals of disease susceptibility than individual constitutional variants or the expression of collections of individual genes. These analyses show emergent, higher-level association of gene variation.


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OCT 01, 2015 1:30 PM PDT

Data, Data, Everywhere - Using Complexity Analysis to find coherence in Cancer Big Data



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