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behaviour of an individual bird (Figure 2). Likewise, Emergent Intelligence (EI) is a global property and surpasses the scope of traditional AI, in that AI is a probabilistic approach requiring large amounts of comparable observations as training sets. Since biological systems are non-lin- ear, there is a general lack of information which AI cannot compensate for. While emergent properties of AGI have been


studied for nearly half a century, very few methods for their identification and analysis exist15. Most of the methods developed using these strategies resort to oversimplification and are not scalable15. Computational and data intense DL methods approach this problem by using modular building blocks such as fully-connected layers, convolutional layers and recurrent layers of networks which are often combined in task-specific ways18. However, the components of biological network layers are not always known and even more ambiguity arises with defining the connections between components since edges in these networks are inducible in a cause and environment specific manner. Thus, con- ventional approaches for DL training sets may be neither physically or economically feasible. In contrast, the methodology introduced in this


paper uses an unsupervised learning approach and examines emergent systems behaviour by determin- ing the routing of information flows induced by pharmacological agents through molecular net- works with overlapping topologies without making assumptions on network links22. Using disease or physiological networks as topological constraints, this methodology identifies the router level connec- tivity linking molecular processes across network layers without making any a priori assumptions on network connectivity or module functions. Enabling this methodology are techniques used in communication network technology topological data analysis and big data analytics23,24. This unsu- pervised learning strategy provides classifications of


pharmacological probes and links between molecu- lar processes associated with system-wide pharma- cology observations. Yielding interdependent cause and effect classifications, this methodology pro- vides self-validating results. Working with informa- tion characteristics instead of content interpreta- tion, this methodology addresses key problems encountered in emergent system analysis. Why does causal emergence matter? Experience


shows that universal reductionism is false when it comes to thinking about causation, and that some- times higher scale reality has more causal influence (and associated information) than whatever under- lies them.


Emergent Behavior Analysis (EBA) SystaMedic Inc’s EBA platform is a novel EI tech- nology to determine data relationships by compar- ing directions of stimuli-induced information flows in dynamic interaction-network systems. This cog- nition enhancement technology uses information characteristics, requires fewer data points, is insen- sitive to noise and reduces system complexity25,26.


Physiology and pathology are regulated by information flows Starting at the body’s smallest scale, the routing of information through the body’s network systems is mediated by proteins which act as environmental sensors27. Detecting changes in local and distant environments (causes), proteins transmit the infor- mation received (effects) by affecting directly or indirectly properties of neighbour proteins (net- work nodes) which, in turn, affect properties of other proteins resulting in protein-protein interac- tion networks that distribute information through- out the body28 (Figure 3). The body’s ability to adapt to changes in environ-


ments relies on the capacity to instantly change the connectivity between sub-networks (topology) con- ducting information flows across all scales of the


Figure 3 EBA platform for analysis of information flows in biological systems


Drug Discovery World Summer 2018


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