Identifying diabetes-related important protein targets with few interacting partners with the PageRank algorithm

Diabetes is a growing concern for the developed nations worldwide. New genomic, metagenomic and gene-technologic approaches may yield considerable results in the next several years in its early diagnosis, or in advances in therapy and management. In this work, we highlight some human proteins that may serve as new targets in the early diagnosis and therapy. With the help of a very successful mathematical tool for network analysis that formed the basis of the early successes of Google TM , Inc., we analyse the human protein–protein interaction network gained from the IntAct database with a mathematical algorithm. The novelty of our approach is that the new protein targets suggested do not have many interacting partners (so, they are not hubs or super-hubs), so their inhibition or promotion probably will not have serious side effects. We have identified numerous possible protein targets for diabetes therapy and/or management; some of these have been well known for a long time (these validate our method), some of them appeared in the literature in the last 12 months (these show the cutting edge of the algorithm), and the remainder are still unknown to be connected with diabetes, witnessing completely new hits of the method.