Integrative Dynamic Analyses of Large Biomedical Network Data List of abstracts

“Avoiding fusion of illusion and confusion – reducing bias in network-based analyses” Protein-protein interactions (PPIs) are essential for understanding signaling cascades, associating proteins with disease or predicting their function, and fathoming drug mechanism of action. However, with estimated only ~10 % of the human PPIs currently known, and about one-third of human proteins without any known interactions, human interactome is highly incomplete and potentially biased. We show that these "orphans " are not a random subset of the proteome, which poses a significant challenge since the current understanding of the interactome may be biased and unreliable. Precision medicine needs to be data driven and corresponding analyses comprehensive and systematic. We will not find new treatments if only testing known targets and studying characterized pathways. Thousands of potentially important proteins remain pathway or interactome "orphans". Computational biology methods can help fill this gap with accurate predictions, but the biological validation and further experiments are essential. Intertwining computational prediction and modeling with biological experiments will lead to more useful findings faster, while optimizing resources. We introduce a data mining-based method to predict physical PPIs proteome-wide. Overall, the in silico predicted interactome comprises 250,542 high confidence physical PPIs among 10,529 huma