Proteins and their interactions have been proven to play a central role in many cellular processes and have been extensively studied so far. However of great importance, little work has been conducted for the identification of biological process interactions in the higher cellular level which could provide knowledge about the high level cellular functionalities and maybe enable researchers to explain mechanisms that lead to diseases. Existing computational approaches for predicting Biological Process interactions used PPI graphs of low quality and coverage but failed to utilize weighted PPI graphs to quantify the quality of the interactions. In the present paper, we propose a unified two-step framework to reach the goal of predicting biological process interactions. After conducting a comparative study we selected as a first step the EVOKALMA model as a very promising algorithm for robust PPI prediction and scoring. Then, in order to be able to handle weights, we combined it with a novel variation of an existing algorithm for predicting biological processes interactions. The overall methodology was applied for predicting biological processes interactions for Saccharomyces Cerevisiae and Homo Sapiens organisms, uncovering thousands of interactions for both organisms. Most of the linked processes come in agreement with the existing knowledge but many of them should be further studied.