Quantifying the complexity of medical research.
Raul Rodriguez-Esteban1, William T Loging
1Computational Biology, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT 06877, USA.
Bioinformatics (Oxford, England)
|September 3, 2013
Summary
Disease research complexity and novelty are increasing, challenging pharmaceutical R&D. Analysis of scientific literature reveals consistent patterns across diseases, regardless of publication volume.
Area of Science:
- Biomedical Research
- Scientific Literature Analysis
- Disease Research Trends
Background:
- Pharmaceutical research and development (R&D) faces diminishing returns.
- Increasing complexity of disease research poses challenges.
- Need to quantify research complexity through literature analysis.
Purpose of the Study:
- To investigate if research complexity can be measured by analyzing published literature.
- To quantify the increasing complexity and novelty in disease research.
Main Methods:
- Text mining of publication records for multiple diseases.
- Analysis of trends in published facts, diversity, and turnover using entropy measures.
- Conceptualizing disease research as a multi-agent search process.
Main Results:
- Disease research complexity and novelty have significantly increased over time.
- Higher publication rates do not correlate with greater disease research complexity or novelty.
- Disease research forms distinct knowledge areas within broader biomedical research.
- Consistent patterns in research complexity and novelty were observed across different disease areas.
Conclusions:
- The complexity and novelty of disease research are rising, impacting pharmaceutical R&D.
- Literature analysis provides a viable method for measuring research complexity.
- Publication volume is not a reliable indicator of research depth or innovation.
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