Gravitation field algorithm and its application in gene cluster.

Area of Science:

• Computational Biology
• Bioinformatics
• Optimization Algorithms

Background:

• Gene clustering is a challenging optimization task in bioinformatics.
• Existing methods like Simulated Annealing (SA) and Genetic Algorithms (GA) are inspired by natural phenomena.
• Biotechnologists utilize these global optimization techniques for analyzing experimental data.

Purpose of the Study:

• Introduce a novel optimization algorithm, the Gravitation Field Algorithm (GFA).
• Evaluate GFA's performance against established algorithms like GA and SA.
• Demonstrate GFA's applicability in gene expression data clustering.

Main Methods:

• Developed the Gravitation Field Algorithm (GFA) based on the Solar Nebular Disk Model (SNDM).
• Applied GFA to optimize multimodal and unimodal functions.
• Utilized GFA for clustering gene expression datasets from the Gene Expression Omnibus (GEO).

Main Results:

• GFA demonstrates superior performance compared to GA and SA on certain multimodal functions.
• GFA effectively clusters gene expression data.
• The algorithm shows versatility in handling both unimodal and multimodal optimization problems.

Conclusions:

• GFA is mathematically proven to converge to a global optimum under specific conditions.
• The study analyzes the global search mechanisms and limitations of SA and GA.
• Source code and results for GFA are publicly available for further research.