This dissertation has tried to tackle three common problems in the field of biomedical research data analytic. The problems that are covered in this document fall around the concept of imbalance and high dimensionality that is in the nature of the datasets that are gathered in this field of research, although they can be extended to other fields as well. Both imbalance and high dimensionality suffer this area of research by impacting the predictive models negatively resulting in introduction of over-fit, or noise in the models. First high dimensionality of the whole blood gene expression arrays are addressed and a new approach using Stability Selection has been proposed to reduce the dimension of these datasets. Then a novel pipeline to combine single-cohort studies into multi-cohort studies is proposed. The pipeline is tested on two GSE datasets which verified the proposed approach. Then, Stability Selection was used to be combined with SMOGN to boost the performance of regression in imbalanced, small, and horizontal datasets. The increase in the regression accuracy enabled further discoveries on AirPICU dataset which and showed the importance of PRISM as a very effective predictor of Ventilation Free Days which indirectly indicates the mortality chance. Finally, the use of unsupervised generative models like CTGAN and TVAE was investigated to reduce the imbalance in imbalanced datasets. It was observed that CTGAN is a powerful model that can improve the performance of SMOTE in imbalance removal both with over-sampling and complete synthetic data modeling.