A review on data mining and continuous optimization applications in computational biology and medicine

Area of Science:

• Computational biology and biotechnology
• Mathematical modeling of biological systems

Background:

• Gene-expression pattern prediction is a growing field requiring mathematical foundations.
• Complex biological systems necessitate advanced data mining and machine learning approaches.

Purpose of the Study:

• To mathematically model and predict gene-expression patterns by integrating environmental factors and uncertainty.
• To review and apply data mining, machine learning, model selection, and kernel learning for biological data analysis.
• To contribute to improved healthcare, medical progress, education, and healthy living.

Main Methods:

• Utilizing matrix and interval arithmetics to incorporate errors and uncertainty into genetic models.
• Extracting nonlinear ordinary differential equations from DNA microarray and environmental data using generalized Chebyshev approximation and semi-infinite optimization.
• Applying combinatorial algorithms to analyze parametric stability and structural stability of gene-environment networks.
• Reviewing model selection and kernel learning for binary classification of microarray data.

Main Results:

• Development of a rigorous mathematical framework for gene-expression modeling under uncertainty.
• Identification of parametric stability regions and analysis of topological landscapes in gene-environment networks.
• Demonstration of machine learning methods for accurate classification of cancerous cells and disease discrimination.

Conclusions:

• The study provides a robust mathematical and computational framework for understanding and predicting gene-expression patterns.
• The proposed methods enhance the analysis of complex biological systems, aiding in disease diagnosis and personalized medicine.
• This research bridges theoretical advancements with practical applications for better health outcomes.