Fabio Fabris1, Alex A Freitas1. 1. School of Computing, University of Kent, CT2 7NF Canterbury, Kent, UK.
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. 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 interpret 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. 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 datasets, at the same time enabling the extraction of knowledge from ageing-related data using quantitative influence information. AVAILABILITY AND IMPLEMENTATION: The datasets created in this paper will be freely available after publication. CONTACT: ff79@kent.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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. 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 interpret 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. 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 datasets, at the same time enabling the extraction of knowledge from ageing-related data using quantitative influence information. AVAILABILITY AND IMPLEMENTATION: The datasets created in this paper will be freely available after publication. CONTACT: ff79@kent.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Anthony Culos; Amy S Tsai; Natalie Stanley; Martin Becker; Mohammad S Ghaemi; David R McIlwain; Ramin Fallahzadeh; Athena Tanada; Huda Nassar; Camilo Espinosa; Maria Xenochristou; Edward Ganio; Laura Peterson; Xiaoyuan Han; Ina A Stelzer; Kazuo Ando; Dyani Gaudilliere; Thanaphong Phongpreecha; Ivana Marić; Alan L Chang; Gary M Shaw; David K Stevenson; Sean Bendall; Kara L Davis; Wendy Fantl; Garry P Nolan; Trevor Hastie; Robert Tibshirani; Martin S Angst; Brice Gaudilliere; Nima Aghaeepour Journal: Nat Mach Intell Date: 2020-10-12
Authors: Gustavo Daniel Vega Magdaleno; Vladislav Bespalov; Yalin Zheng; Alex A Freitas; Joao Pedro de Magalhaes Journal: BMC Bioinformatics Date: 2022-01-04 Impact factor: 3.169