Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond

Abstract

A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.

Optimized network based natural language processing approach to reveal disease comorbidities in COVID-19

“…SARS-CoV-2 virus could infect many types of human cells, and also has the ability of rapid evolution. Even though numerous strains of the virus have been identified 1 , 2 , the virus's infection process in all strains relies on the ACE2 receptor protein binding to spike proteins as reported by the early investigations of the infection 3 . The vascular effects of the ACE/ACE2 balance were also found to be influenced by viral binding, leading to several complications.…”