Predictions in biometric models

Abstract

One of the domains of genetic enhancement that has extensively employed both simulation and authentic data is Biometrics. Selecting efficient models for the Genome-Wide Selection (GWS) process using molecular markers (SNPs) presents several challenges. Among these challenges is the effective identification of the optimal model for fitting a given dataset. To contribute to this endeavor, this paper's primary objective is to assess the predictive accuracy of nine (9) distinct models, each following different paradigms within the realm of Biometrics. The data employed in this study were generated through simulation, encompassing the primary issues encountered in this field of research, including high dimensionality, nonlinearity, and multicollinearity. As the primary findings, notable observations include the enhancement of predictive efficiency as data noise decreases, the predominance of the tree paradigm (for low noise levels, BOO), and the efficacy of the neural network paradigm (for high noise levels, RBF).

Keywords

• machine learning; genomic analysis; simulation; SNP.