ImmunAL: a frame to identify the immunological markers for Mild Moderated Alzheimer's Disease applying Multiplex Network Model

Abstract

Identification of immunological markers for neurodegenerative diseases resolve issues related to diagnostic and therapeutic. Neuro-specific cells experience disruptive mechanisms in the early stages of disease progression. The autophagy mechanism, guided by the autoantibodies, is one of the prime indicators of neurodegenerative diseases. Identifying autoantibodies can show a new direction. Detecting influential autoantibodies from relational networks viz., co-expression, co-methylation, etc. is a well-studied area. However, none of the studies have considered the functional affinity among the autoantibodies while selecting them from a relational network. In this regard, a twolayered multiplex network based framework has been proposed, whereby the layers consist co-expression and co-semantic scores. The networks have been formed using three distinct cases viz., diseased, controlled, and a combination of both. Subsequently, a random walk with restart mechanism has been applied to identify the influential autoantibodies, where layer switching probability and restart probability are 0.5 and 0.4 respectively. Next, pathway semantic network has been formed considering the autoantibody associated pathways. EPO and IL1RN, associated with a maximum number of pathways, are identified as the two most influential autoantibodies. The network also provides insights into possible molecular mechanisms during the pathogenic progression. Finally, MDPI and CNN3 are also identified as important biomarkers. AvailabilityThe code is available at https://github.com/agneet42/ImmunAL