Maritza Ramirez

Machine learning (ML) models make decisions for governments, companies, and individuals. Accordingly, there is the increasing concern of not having a rich explanatory and predictive account of the behaviour of these ML models relative to the users’ interests (goals) and (pre-)conceptions (ontologies). We argue that the recent research trends in finding better characterisations of what a ML model does are leading to the view of ML models as complex behavioural systems. A good explanation for a model should depend on how well it describes the behaviour of the model in simpler, more comprehensible, or more understandable terms according to a given context. Consequently, we claim that a more contextual abstraction is necessary (as is done in system theory and psychology), which is very much like building a subjective mind modelling problem. We bring some research evidence of how this partial and subjective modelling of machine learning models can take place, suggesting that more machine learning is the answer.

The Wnt/β-catenin pathway is constitutively activated in more than 90% of human colorectal cancer. Activated β-catenin stimulates cell proliferation and survival, however, its antiapoptotic mechanisms are not fully understood. We show here that activated β-catenin is required to suppress caspase-8 activation, but only in colon cancer cells that are resistant to tumor necrosis factor-a -induced apoptosis. We found that lysosomal delivery of internalized TNF occurred at a faster pace in apoptosis-resistant than in apoptosis-sensitive colon cancer cells. Retardation of endosomal trafficking through vacuolar ATPase inhibition enhanced caspase-8 activation in apoptosis-resistant but not apoptosis-sensitive cells. Interestingly, knockdown of β-catenin also prolonged TNF association with the early endosome and enhanced caspase-8 activation in apoptosis-resistant but not apoptosis-sensitive colon cancer cells. In a mouse model of inflammation-associated colon tumors, we found nuclear expression of β-catenin, resistance to TNF-induced apoptosis, and reactivation of apoptosis in vivo after cotreatment of TNF with a V-ATPase inhibitor. Together these results suggest that activated β-catenin can facilitate endosomal trafficking of internalized TNF to suppress caspase-8 activation in colon cancer cells. © 2013 Han et al.

© 2015 Thessen et al.Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and two proposed example data models for linking phenotypes and environments using the extensible observation ontology and the Biological Collections Ontology ; these provide a starting point for the development of a data model linking phenotypes and environments.