Yatiri Bio develops proprietary computational tools (differential expression, pathway enrichment, sample outliers and other analyses) that employ machine learning techniques such as unsupervised clustering and dimensionality-reduction methods to specifically explore proteomics data in the context of human biology. We move beyond the statistical modeling by employing our vast experience and deep understanding of experimental design with the goal of utilizing all elements of the captured data and converting it to actionable knowledge. The result of this work is a fully interactive data portal ProteoBrowser and ProteoPathway tailored for each experiment, and optimized with collaborative iterations.