Systems Biology at Scale
The Comprehension Normalization Method for Networks is able to extract functionally related subnetworks, using very little starting information, and do so over 1000 times faster than the previous cutting edge methods. These functionally related subnetworks are a complex form of causation beyond even interdependent variables, and is caused by causal systems. In high dimensional data housing these causal systems, the functionally related subnetworks might be drivers of disease and potentially treatments and cures.
- CNM for Networks is software that works on any organic network, regardless of the discipline
- Not a black box - makes key systems highly visible to enable dynamic control
- Rapid time to insight
Over 1000 times faster
- CNM
- Traditional Integration Derived/Constructed Causal Networks
- CNM
- Traditional Integration Derived/Constructed Causal Networks
Unlike traditional machine-learning network analysis where the complexities remain hidden in a black box only accessible to a computer which can give a prediction, the Comprehension Normalization Method empowers the scientist with knowledge of the causal systems either to manipulate (to change the results) with new therapeutics or to flexibly monitor information by causal systems to dynamically inform the course of action.
The Power of Systems Causation
Systems Biology able to be Studied at Scale
- Efficiently manage and govern systems
- Flexibility
- control of specific system driven/disease causing functions