New KEGG pathway-based interpretable features for classifying ageing-related mouse proteins

Abstract

Motivation: The incidence of ageing-related diseases has been constantly increasing in the last decades, raising the need for creating effective methods to analyze ageing-related protein data. These methods should have high predictive accuracy and be easily interpretable by ageing experts. 15 To enable this, one needs interpretable classification models (supervised machine learning) and features with rich biological meaning. In this paper we propose two interpretable feature types based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and compare them with traditional feature types in hierarchical classification (a more challenging classification task regarding predictive performance) and binary classification (a classification task producing easier to inter-20 pret classification models). As far as we know, this work is the first to: (i) explore the potential of the KEGG pathway data in the hierarchical classification setting, (i) use the graph structure of KEGG pathways to create a feature type that quantifies the influence of a current protein on another specific protein within a KEGG pathway graph and (iii) propose a method for interpreting the classification models induced using KEGG features. 25 Results: We performed tests measuring predictive accuracy considering hierarchical and binary class labels extracted from the Mouse Phenotype Ontology. One of the KEGG feature types leads to the highest predictive accuracy among five individual feature types across three hierarchical classification algorithms. Additionally, the combination of the two KEGG feature types proposed in this work results in one of the best predictive accuracies when using the binary class version of our 30 datasets, at the same time enabling the extraction of knowledge from ageing-related data using quantitative influence information.

A review of supervised machine learning applied to ageing research

“…2015; Fang et al. 2013; Fabris and Freitas 2016); nutrient receptor genes (Fabris et al. 2016; Wan et al.…”

“…2011), that is, the variable can take only two possible discrete values. Others deal with hierarchical classification problems (e.g., Fabris and Freitas 2016), where takes nominal or discrete values that are organised into a pre-defined hierarchy. Some works treat the problem as a regression task rather than a classification task (e.g., Nakamura and Miyao 2007).…”

“…To define predictor attributes, some works use low-level features derived from “raw” amino-acid sequences of ageing-related proteins (e.g., Fabris and Freitas 2016). Other works use biomarkers of higher-level biological systems like metabolic and renal systems (e.g., Putin et al.…”

“…In Fabris and Freitas (2016) the authors consider as positive instances proteins that are involved in increased mortality and ageing, and as negative instances proteins that are involved in mortality and not involved in ageing. It is interesting to study what differentiates these two classes, since some mutations reduce the lifespan of organisms (e.g., they increase the incidence or lethality of some diseases) but are believed not to be related to ageing.…”

“…In Fabris and Freitas (2016), for mouse, it was found that if a protein has any influence on CDK1 (associated with apoptosis), then that protein is more likely to be ageing-related. CDK1 is putatively associated to ageing in mice (Xiao et al.…”