Community-driven roadmap for integrated disease maps

Abstract

The Disease Maps Project builds on a network of scientific and clinical groups that exchange best practices, share information and develop systems biomedicine tools. The project aims for an integrated, highly curated and user-friendly platform for disease-related knowledge. The primary focus of disease maps is on interconnected signaling, metabolic and gene regulatory network pathways represented in standard formats. The involvement of domain experts ensures that the key disease hallmarks are covered and relevant, up-to-date knowledge is adequately represented. Expert-curated and computer readable, disease maps may serve as a compendium of knowledge, allow for data-supported hypothesis generation or serve as a scaffold for the generation of predictive mathematical models. This article summarizes the 2nd Disease Maps Community meeting, highlighting its important topics and outcomes. We outline milestones on the roadmap for the future development of disease maps, including creating and maintaining standardized disease maps; sharing parts of maps that encode common human disease mechanisms; providing technical solutions for complexity management of maps; and Web tools for in-depth exploration of such maps. A dedicated discussion was focused on mathematical modeling approaches, as one of the main goals of disease map development is the generation of mathematically interpretable representations to predict disease comorbidity or drug response and to suggest drug repositioning, altogether supporting clinical decisions.

Keywords: biocuration; disease maps; knowledge repository; mathematical modeling; molecular biology; pathway representation; translational medicine.

Figure 1

The milestones of the DMC roadmap. Five groups of topics are highlighted. Tools: Software and methods supporting the development and maintenance of the maps; Biocuration standards: standards for knowledge gathering and encoding in the maps; Complexity management: methods that handle inherent complexity and facilitate visual exploration of the contents of the maps; Applications: workflows where maps can be applied to support knowledge exploration, generation of new hypotheses or support clinical decisions; and Modeling: standards and tools allowing to refine the maps into executable mathematical models.

Figure 2

A life cycle of a disease map with the roadmap milestones. The figure illustrates the life cycle of a disease map, starting from the biocuration based on the relevant literature and available pathway databases. This knowledge is synthesized into a comprehensive repository: the disease map. Data interfaces and links to biomedical databases, together with accessible, visualized content allow for informed interpretation toward knowledge exploration, generation of new hypotheses or clinical decision support. The outcomes of the interpretation step link back to particular phases of the life cycle. ‘Data interfaces’ feedback describes the possibility of interconnecting additional data sources for better interpretation. ‘Synthesis’ feedback indicates improved knowledge organization within the disease map. ‘Biocuration’ feedback means introduction of new, validated hypothesis about the disease-related mechanisms. Notes: Milestones discussed for the Disease Maps Roadmap are mapped on the diagram as follows: T: Tools, T1: Modeling-oriented curation, T2: Visualization of simulation results, T3: Information exchange between maps; B: Biocuration standards, B1: Knowledge quality indicators, B2: Review of the text mining support, B3: On-the-fly consistency check, B4: Connecting mechanisms and disease hallmarks; C: Complexity management, C1: Dynamic subnetwork collapsing, C2: Algorithms for layered scale complexity, C3: Methods for dynamic layouts, C4: Handling large diagrams; A: Applications, A1: Cross-linking disease maps and pathway databases, A2: Data-based tissue specificity, A3: Data interpretation pipelines, A4: Quality assessment via in silico replication; M: Modeling, M1: Minimal information set for modeling, M2: Database of general and disease-specific data, M3: Scalable computational pipeline for models, M4: Model-based individualization approaches.