Literature Lab: A Method for Automated Literature Analysis
Author Information
Author(s): Phillip G Febbo, Mike G Mulligan, David A Slonina, Kimberly Stegmaier, Dolores Di Vizio, Paul R Martinez, Massimo Loda, Stephen C Taylor
Primary Institution: Duke University
Hypothesis
Can automated literature interrogation identify biological associations from microarray data?
Conclusion
Literature Lab can effectively discover key biological processes by identifying associations between gene sets and terms in the biomedical literature.
Supporting Evidence
- Literature Lab identified a strong association between the pathway term 'FOSB' and genes in metastatic prostate cancer.
- Immunohistochemistry confirmed increased nuclear FOSB staining in metastatic compared to locally invasive prostate cancers.
- Literature Lab was tested on gene sets from multiple cancer experiments.
Takeaway
Literature Lab is like a smart robot that reads a lot of science papers to find important connections between genes and diseases.
Methodology
Literature Lab uses automated literature searches to find associations between gene sets and key terms from curated lists.
Potential Biases
Potential biases may arise from the selection of gene sets and key terms used in the analysis.
Limitations
The method may be affected by literature drift, which can change the number of associations identified over time.
Participant Demographics
The study involved gene sets derived from cancer experiments, including human tumor samples.
Statistical Information
P-Value
0.0013
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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