An MVC-inspired Approach for an Intelligent Annotation of a Protein Ontology : IA-PrOnto

Abstract

Current medicine has recently recognized the limits of delivering the same treatment to different patients with the same disease. Although, for a long time, clinicians have adjusted patient’s treatment according to several parameters: gender, age, weight, etc.., response rate still varies from 20% to 80% for these conventional therapies. A new medicine has therefore been developed, which involves the design of specific treatments based on the individual biological characteristics of the patient (i.e. genetic and protein information). DNA sequencing was one of these new approaches that have been developed for obtaining and analyzing genetic information. While this newly available genetic information has opened new avenues for applying personalized medicine, some issues remain to be addressed. One of these issues concerns the availability of the protein information. Therefore, and in order to provide structured knowledge about proteins, our previous research has investigated the dynamic development of a Protein Ontology: PrOnto. And in this paper, we propose an intelligent annotation approach which dynamically enrich PrOnto, the annotation method was inspired by the MVC pattern where the Model is the Protein Ontology, the View is an Intelligent User Interface that semi-automatically annotates PrOnto and the Controller is an intelligent agent that automatically annotates the Protein Ontology. Moreover, the proposed approach enables the automatic prediction of the 2D and the 3D protein structures, which will allow providing all protein information needed to annotate PrOnto with more reliable knowledge.