Introduction: Computational biology, diagnostic modalities, clinical patient results often involve working with high-dimensional data (p >> n). Penalized regression methods are often used on such data, as they can perform feature selection effectively. In particular, network penalized approaches also allow to model relationships between predictors, as often occurs when analyzing omics data. Unfortunately, the wide variety of such methods in literature leads to difficulty in choosing the most suitable one for a given dataset.
Aim: This paper focuses on a variety of regression methods and potential applications in computational biology and medical research.
Materials and Methods: The following basic regression methods are briefly discussed: Ridge, Lasso, Elastic Net, and Grace. Their application in solving problems in medical research as well as in everyday practice of the Modelling and Simulation research group at Medical University of Varna is demonstrated.
Results and Conclusion: Regression methods were successfully used to define the characteristics of the available materials with 3D printing technologies. Based on these, novel physical models were manufactured and for x-ray imaging research use. Application of network-based regularization method was reviewed to be suitable for defining the relation between patient’s genotype and drug response.
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