Modern biology increasingly relies on high-throughput techniques, such as proteomics, metabolomics, and transcriptomics, which generate vast amounts of data. A common challenge with these techniques is interpreting the outcomes, as individual measurements vary in quality. Our mission is to enhance the yield and facilitate the interpretation of high-throughput experiments by employing various machine learning methods, including Deep Leaning and Hierarchical Bayesian Networks.