Open Collections

Predictive Integration of Networked Big Data

Banff International Research Station for Mathematical Innovation and Discovery

We are faced with a flood of molecular, clinical, economic and other data. Various biomolecules interact in a cell to perform biological function, forming large, complex systems. The challenge is how to mine these molecular systems to answer fundamental questions, including gaining new insight into diseases and improving therapeutics. Just as computational approaches for analyzing genetic sequences have revolutionized biological understanding, the expectation is that analyses of large-scale, networked “omics” data will have similar ground-breaking impacts. However, dealing with these data is nontrivial, since many methods for analyzing large networks fall into the category of computationally intractable problems. We develop methods for extracting new biomedical knowledge from the wiring patterns of large molecular and patient data, linking molecular network wiring with biological function and disease, hence translating the information hidden in the wiring patterns into domain knowledge. We apply our methods to other domains, including tracking the dynamics of the world trade network and finding new insights into the origins of wealth and economic crises. Our new methods stem from network science approaches coupled with graph-regularized non-negative matrix tri-factorization, a machine learning technique for co-clustering heterogeneous datasets.

Mathematics; Statistics; Biology and other natural sciences

video/mp4

Author affiliation: University College London

2017-09-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/63123

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/