THEME 1: Improving the Experimentation and Discovery Process

Unprecedented complexity of scientific enterpriseIs science stymied by the human bottleneck?

The Research Process – cyclic • Support for designing the experiment

(or study)– Identify controls– Inventory materials/equipment– Protocols– Statistics, comp tools

• Support for executing the experiment (or study)– Get funding– Adaptive /real time

experimentation– Integrative interpretation

• Analyzing/exploring/validate the data• Interpreting the results

• Collaborative analysis• Putting the results in context• Communicating and • Prioritizing the next thing

• Make assumptions through background knowledge (combination of existing knowledge) via– Literature– Data– Collaboration

• Internalization -> idea(s)• Consider the

importance/novelty/feasibility/cost/risk of the idea(s)

• Formulate testable hypothesis(s)• Make consistent/validate with/against

existing knowledge

3

To advance understanding about a biological system, the usual starting point is the hypothesis, or model, constructed and refined using available information about the biological system. Refined hypotheses are subjected to experimental testing and hypotheses that survive this validation are shared, generally through publication.

Making Biological Computing Smarter - The Scientist - Magazine of the Life Sciences http://www.the-scientist.com/article/display/15508/#ixzz1lGQM6R41

Global Needs

• Appropriate metadata at all stages of the process

• Usability, Accessibility, Reproducibility

• Collaboration – developers and consumers must both be engaged in the process.– Form a community around the

process– Characterize the community and

the explicit process– Need scalable/generalizable tools

and specialized tools that map to explicit processes

• Formal representation of the knowledge linked to the data and metadata

• Value metrics/reward system

• Getting a better grasp on what has succeeded and what has failed– What has worked and what has

not– Education regarding usability,

human computer interaction

• Improved methods for abductive inference

KR and computation complexity

Virtual soldier

TMJ

HyBrow

TAMBIS Riboweb BioLingua

Biocyc BioSigNet Pathway logic

Mycin

PharmGKB

Reactome

Use of GO

High KR complexity

Minimal KR complexity

Minimal computational complexity

High computational complexity

Actions

– Make assumptions through background knowledge (combination of existing knowledge) via• Literature• Data• Collaboration• Social media

• Find a knowledge association

• Mendeley• Assimilation of

information (eg neuroscience information framework)

• More sophisticated methods for assessing a search result

• Filtering of social media