Computational dynamic approaches for temporal omics data with applications to systems medicine.

Area of Science:

• Systems and computational biology
• Systems medicine

Background:

• Modeling biological dynamics and estimating kinetic parameters are crucial for understanding human health, drug response, and disease.
• Temporal omics data are essential for discovering biological interactions and clinical mechanisms but are challenging to analyze.
• Conventional experimental techniques are insufficient for analyzing big omics data in the current era.

Purpose of the Study:

• To present recently developed dynamic trajectory and causal network approaches for temporal omics data.
• To provide a guide for researchers entering this complex field.
• To discuss applications, state-of-the-art performance, and challenges in analyzing temporal omics data.

Main Methods:

• Review of dynamic trajectory approaches for temporal omics data.
• Review of causal network approaches for temporal omics data.
• Critical discussion of merits, drawbacks, and limitations of presented methods.

Main Results:

• Various recently developed dynamic trajectory and causal network approaches are presented.
• Applications to biological systems, health conditions, and disease status are discussed.
• State-of-the-art performances for different mining tasks are summarized.

Conclusions:

• The presented methods offer valuable tools for analyzing temporal omics data.
• Critical discussion highlights limitations and future challenges in the field.
• Recent computing tools and software resources are detailed for practical application.