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Complex Insight - Understanding our world
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A few things the Symbol Research team are reading. Complex Insight is curated by Phillip Trotter (www.linkedin.com/in/phillip-trotter) from Symbol Research
Curated by
Phillip Trotter
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Scoop.it!
A proof-of-principle prototype for CancerLinQ™ was demonstrated at the 2013 ASCO Annual Meeting. This “learning health system” prototype was first unveiled on March 27 at an ASCO-hosted panel discussion on big data in cancer care at the National Press Club in Washington, DC. At the ASCO Annual Meeting, attendees were able to view a demonstration of the CancerLinQ prototype in the exhibit hall as well as attend an educational session on the product, which ASCO calls the first demonstration of the feasibility of a health information technology-based learning health system. - See more at: http://www.onclive.com/publications/oncology-business-news/2013/September-2013/CancerLinQ-Proof-of-Principle-Prototype#sthash.AoXUoYrD.dpuf
Phillip Trotter's insight:
Effective data analysis is going to be key in improving cancer treatment and the outcomes of the CancerLinQ project is a key first step in demonstrating potential solutions in this area. 
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From
arxiv
Real world network datasets often contain a wealth of complex topological information. In the face of these data, researchers often employ methods to extract reduced networks containing the most important structures or pathways, sometimes known as `skeletons' or `backbones'. Numerous such methods have been developed. Yet data are often noisy or incomplete, with unknown numbers of missing or spurious links. Relatively little effort has gone into understanding how salient network extraction methods perform in the face of noisy or incomplete networks. We study this problem by comparing how the salient features extracted by two popular methods change when networks are perturbed, either by deleting nodes or links, or by randomly rewiring links. Our results indicate that simple, global statistics for skeletons can be accurately inferred even for noisy and incomplete network data, but it is crucial to have complete, reliable data to use the exact topologies of skeletons or backbones. These results also help us understand how skeletons respond to damage to the network itself, as in an attack scenario.
Robustness of skeletons and salient features in networks http://arxiv.org/abs/1309.3797 Via Complexity Digest, Eugene Ch'ng
Phillip Trotter's insight:
Very relevent to some current work we are doing on data modeling and data mining - Awesome scoop - big thanks Eugene and Complexity Digest..
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Use Google Flu Trends as an example of how things go wrong.
Phillip Trotter's insight:
Good article on risks of interpreting large scale data mining using google flu trends as an example. The conclusion that often data indications are a first point of a process that then requires scientific grunt work to identify if a given correlation is actually relevant or not and that reality is sometimes the grunt work wont be done because its hard, requires funding and takes time. Worth reading.
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GDELT: Global Database of Events, Language, and Tone
Phillip Trotter's insight:
The Global Database of Events, Language, and Tone (GDELT) is an initiative to construct a catalog of human societal-scale behavior and beliefs across all countries of the world over the last two centuries down to the city level globally, to make all of this data freely available for open research, and to provide daily updates to create the first "realtime social sciences earth observatory." Nearly a quarter-billion georeferenced events capture global behavior in more than 300 categories covering 1979 to present with daily updates. Data is available for data scientists to mine and analyze - See more at: http://gdelt.utdallas.edu/#sthash.04oK6DrA.dpuf
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Dr.Stefan Gruenwalds scoop-it list of bioinformatics databases is an essential resource for anyone looking to discover which datasets are available and accessible. Awesome.