Comorbidity scoring with causal disease networks - Jhee, Jong Ho; Bang, Sunjoo; Lee, Dong Gi; Shin, Hyunjung (IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2019)
In recent years, there has been numerous studies constructing a disease network with diverse sources of data. Many researchers attempted to extend the usage of the disease network by employing machine learning algorithms on various problems such as prediction of comorbidity. The relations between diseases can further be specified into causal relations. When causality is laid on the edges in the network, prediction for comorbid diseases can be more improved. However, not many machine learning algorithms have been developed to concern causality. In this study, we exploit a network based machine learning algorithm that generates comorbidity scores from a causal disease network. In order to find comorbid diseases, semi-supervised scoring for causal networks is proposed. It computes scores of entire nodes in the network when a specific node is labeled. Each score is calculated one at a time and affects to the others along causal edges. The algorithm iterates until it converges. We compared the scoring results of the causal disease network and those of simple association network. As a gold standard, we referenced the values of relative risk from prevalence database, HuDiNe. Scoring by the proposed method provides clearer distinguishability between the top-ranked diseases in the comorbidity list. This is a benefit because it allows the choosing of the most significant ones on an easier fashion. To present typical use of the resulting list, comorbid diseases of Huntington disease and pnuemonia are validated via PubMed literature, respectively.
Keywords: Causality; Co morbidities; Manufacturing industries; Semi- supervised learning; Symmetric matrices; Algorithms; Causality; Comorbidity; Computational Biology; Databases, Factual; Epidemiology; Humans; Models, Biological; Risk; Supervised Machine Learning; Causality; comorbidity; disease network; semi-supervised learning