GRAMP: A gene ranking and model prioritisation framework for building consensus genetic networks

• Determining the best method for a specific inference problem of gene regulatory networks is challenging. • Existing ensemble methods are a solution but lack a sophisticated network aggregation method. • Developing a reliable mechanism to build consensus networks is important by identifying the most effective methods in a given problem context. • A novel network aggregation method called GRAMP, Gene Ranking And Model Prioritisation framework is developed as a solution. Despite significant recent advancements in computational and statistical methods, different methods have specific strengths and weaknesses in the accurate reconstruction of gene regulatory networks (GRNs), making it difficult to determine the best method for each specific problem. To overcome these challenges, ensemble approaches, which combine the strengths of individual inference methods, are valuable. However, existing ensemble methods for GRN inference lack a sophisticated network aggregation method and generally rely solely on ranking approaches. These ensemble methods have no reliable mechanisms to identify highly performing inference methods specific to a given problem. They therefore tend to aggregate weak methods, diminishing the overall accuracy of the approach. Thus, developing a reliable mechanism to identify the most effective methods for specific problems and prioritize them in consensus network building is important. This paper presents a novel ensemble approach for reconstructing GRNs by integrating previously developed diverse GRN inference approaches. A novel network aggregation method called GRAMP, Gene Ranking And Model Prioritisation framework was developed, taking into consideration both local and global gene ranking and the performance of different inference approaches on a specific network. The proposed ensemble approach demonstrated performance superior to those of other state-of-the-art methods, as evidenced by results from simulated datasets and a real-world gene expression dataset.