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Figure 2 Emergent behaviour !"#$%&' ()!* '(&( 0%0$1023&1 1+'.#+&! !3$4,34$0& (+',-&' .)$/! '(&.


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from large amounts of information; however, by itself it struggles to provide value when the infor- mation is sparse. This can be readily seen by our inability to model rare orphan diseases that lack well-known information that is relevant to the research question being asked. AI applications require creation of training data which requires human experts for interpreting lots of data to build complex views of the problem. Since getting the right input data is for the right job is expensive, real-world data are frequently replaced with machine-created data that tend to be error-prone. The best approach for constructing these train-


ing sets (supervised learning) requires human experts who can interpret lots of data for building complex views of problem and intention spaces to be solved13. Adding complexity to this task, these experts would need to consider how changes in environments affect emergent causalities and behaviour14. Misrepresentation of these cause- effect relationships in training set construction will create cascading negative consequences15. Considering these complexities, it is not surprising that most examples of emergent systems analysis use models that are hardly ever found in real life and are not scalable15,16.


SystaMedic Inc’s innovative Emergent Intelligence (EI) technology Emergent Behavior Analysis (EBA) We believe that AI’s success will depend on its abil- ity to model behaviour of biological systems that inherently display emergent properties17. Emergence refers to the ability of low-level compo- nents of a system or community to self-organise into a higher-level system of sophistication and awareness1. However, emergent behaviour cannot be computed by summing up the workings of a sin-


46


gle cell and isolated molecular circuits1. Biological systems are made up of integrated networks of organelles which form cell networks which again form organ networks and so on18-20. For regulat- ing systems behaviour, these interconnected net- work layers are in constant dialogue and this, in turn, makes and breaks connections between net- work layers21. This paper introduces a cutting-edge approach


for the Analysis of Emergent Behavior of complex biological systems. This novel methodology identi- fies, amongst infinite number of combinations of protein interactions, network connectivity that is important for regulating information flows through networks of networks controlling the body’s response to diseases and drug treatments. Overcoming the barriers of probabilistic data ana- lytics, EBA’s information theory-based approach is especially useful for drug repositioning, bio- prospecting of plants, herbs, traditional medicines, identification of efficacy and safety biomarkers and the design of ‘smart and targeted’ clinical trials and toxicological studies. Moreover, EBA has been shown to be a powerful technology for developing targeted product profiles, competitive analysis of drugs and therapeutic areas, and the prediction of drug-drug interactions. We describe its use as a cognition enhancement approach for identifying novel agents that can modulate resistance of bacte- ria to antibiotics.


Analysis of emergent behaviour in complex biological systems Emergent behaviour is any behaviour of a system that is not a property of any of the components of that system. That is, a property that emerges due to interactions among the components of a system, as mentioned below. For example, flocking is not the


Drug Discovery World Summer 2018


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