Abstract

Scientific data is continuously generated throughout the world. However, analyses of these data are typically performed exactly once and on a small fragment of recently generated data. Ideally, data analysis would be a continuous process that uses all the data available at the time, and would be automatically re-run and updated when new data appears. We present a framework for automated discovery from data repositories that tests user-provided hypotheses using expert-grade data analysis strategies, and reassesses hypotheses when more data becomes available. Novel contributions of this approach include a framework to trigger new analyses appropriate for the available data through lines of inquiry that support progressive hypothesis evolution, and a representation of hypothesis revisions with provenance records that can be used to inspect the results. We implemented our approach in the DISK framework, and evaluated it using two scenarios from cancer multi-omics: 1) data for new patients becomes available over time, 2) new types of data for the same patients are released. We show that in all scenarios DISK updates the confidence on the original hypotheses as it automatically analyzes new data.

Metadata

publication

Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017

year

2017

publication date

2017/2/12

authors

Yolanda Gil, Daniel Garijo, Varun Ratnakar, Rajiv Mayani, Ravali Adusumilli, Hunter Boyce, Arunima Srivastava, Parag Mallick

link

https://ojs.aaai.org/index.php/AAAI/article/view/11157

resource_link

https://ojs.aaai.org/index.php/AAAI/article/download/11157/11016

journal

Proceedings of the AAAI Conference on Artificial Intelligence

volume

31

issue

1