2014 AutomatedHypothesisGenerationba
- (Spangler et al., 2014) ⇒ Scott Spangler, Angela D. Wilkins, Benjamin J. Bachman, Meena Nagarajan, Tajhal Dayaram, Peter Haas, Sam Regenbogen, Curtis R. Pickering, Austin Comer, Jeffrey N. Myers, Ioana Stanoi, Linda Kato, Ana Lelescu, Jacques J. Labrie, Neha Parikh, Andreas Martin Lisewski, Lawrence Donehower, Ying Chen, and Olivier Lichtarge. (2014). “Automated Hypothesis Generation based on Mining Scientific Literature.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623667
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- http://scholar.google.com/scholar?q=%222014%22+Automated+Hypothesis+Generation+based+on+Mining+Scientific+Literature
- http://dl.acm.org/citation.cfm?id=2623330.2623667&preflayout=flat#citedby
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Abstract
Keeping up with the ever-expanding flow of data and publications is untenable and poses a fundamental bottleneck to scientific progress. Current search technologies typically find many relevant documents, but they do not extract and organize the information content of these documents or suggest new scientific hypotheses based on this organized content. We present an initial case study on KnIT, a prototype system that mines the information contained in the scientific literature, represents it explicitly in a queriable network, and then further reasons upon these data to generate novel and experimentally testable hypotheses. KnIT combines entity detection with neighbor-text feature analysis and with graph-based diffusion of information to identify potential new properties of entities that are strongly implied by existing relationships. We discuss a successful application of our approach that mines the published literature to identify new protein kinases that phosphorylate the protein tumor suppressor p53. Retrospective analysis demonstrates the accuracy of this approach and ongoing laboratory experiments suggest that kinases identified by our system may indeed phosphorylate p53. These results establish proof of principle for automated hypothesis generation and discovery based on text mining of the scientific literature.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2014 AutomatedHypothesisGenerationba | Ying Chen Scott Spangler Angela D. Wilkins Benjamin J. Bachman Meena Nagarajan Tajhal Dayaram Peter Haas Sam Regenbogen Curtis R. Pickering Austin Comer Jeffrey N. Myers Ioana Stanoi Linda Kato Ana Lelescu Jacques J. Labrie Neha Parikh Andreas Martin Lisewski Lawrence Donehower Olivier Lichtarge | Automated Hypothesis Generation based on Mining Scientific Literature | 10.1145/2623330.2623667 | 2014 |