- 1 Overview
- 2 Challenges by field
- 3 Goals for the working group
- 4 Strategy for achieving goal
- 5 Progress in achieving goals
What new kinds of evolutionary analyses will we see in the future, and how can we look ahead to facilitate these new kinds of analysis? What are the implications of new kinds of "-omics" data? What are the implications for phylogeography of the flood of data from GPS technology?
For instance, the problem of "functional inference" in genomics is one area where quick-and-dirty methods or generic machine-learning approaches (all based on variations of guilt-by-association) are giving way to more robust model-based evolutionary approaches. This is only natural because evolutionary methods are powerful: they were developed by people who were trying to squeeze every last bit of information from scarce and expensive data.
Where is the next frontier to be invaded by evolutionary methods? What will be the new demands?
Challenges by field
Near-term challenges: What are the problems that experts are addressing with evolutionary methods today at the very cutting edge of research? If the evolutionary approach is successful, then tomorrow the demand will rise for more powerful and automated tools.
Longer-term challenges: what kinds of new problems will arise in the future to be addressed by evolutionary methods? If its hard to state the problem clearly, think of what will be the inputs and outputs.
Genomics, proteomics, and systems biology
functional inference, networks?
Medicine, health, epidemiology
pathogen identification, biomarkers?
Ecology, Conservation Biology, and Phylogeography
GPS data, anyone?
phylogenetic image analysis?
Cross-cutting (interdisciplinary) challenges
Goals for the working group
(specific goals for this topic)
Strategy for achieving goal
(be sure to include specific deliverables or milestones)
Progress in achieving goals
(give dates and provide links to outputs)