PanoromiX: a time-course network medicine platform integrating molecular assays and pathophenotypic data

Area of Science:

• Network Medicine
• Computational Biology
• Bioinformatics

Background:

• Network medicine integrates molecular data with disease phenotypes using complex networks.
• Understanding disease progression requires analyzing the temporal dynamics of these networks.
• Current platforms lack the ability to modularize complex biological systems and integrate diverse data for interactive visualization.

Purpose of the Study:

• To develop an advanced platform for dynamic, interactive visualization of molecular and phenotypic data in network medicine.
• To enable the integration of genome-based assay outputs with time-course pathophenotypic data.
• To provide a user-friendly tool for exploring complex disease networks without extensive programming knowledge.

Main Methods:

• Development of PanoromiX, a data-agnostic, dynamic, interactive web application.
• Visualization of genome assay outputs onto modular networks correlated with pathophenotypic data over time.
• Integration of gene regulatory network analysis with disease progression data.

Main Results:

• PanoromiX enables visualization of molecular assay results on interactive networks linked to phenotypic data across a time course.
• The platform reveals disease pathology through gene regulatory network dynamics, offering insights beyond static assay snapshots.
• Users can design, share, and interpret dynamic networks with built-in functionalities, regardless of programming expertise.

Conclusions:

• PanoromiX is the first tool to visualize genome assays, pathological networks, and phenotypes in a data-agnostic manner for network medicine.
• It facilitates monitoring biomarker perturbations, classifying diseases by network modules, and exploring molecular interactions.
• The platform supports the discovery of novel diagnostic and therapeutic targets by analyzing dynamic network changes in disease states.