MOTIVATION: Network-based driver identification methods that can exploit mutual exclusivity typically fail to detect rare drivers because of their statistical rigor. Propagation-based methods in contrast allow recovering rare driver genes, but the interplay between network topology and high-scoring nodes often results in spurious predictions. The specificity of driver gene detection can be improved by taking into account both gene-specific and gene-set properties. Combining these requires a formalism that can adjust gene-set properties depending on the exact network context within which a gene is analyzed. RESULTS: We developed OMEN: a logic programming framework based on random walk semantics. OMEN presents a number of novel concepts. In particular, its design is unique in that it presents an effective approach to combine both gene-specific driver properties and gene-set properties, and includes a novel method to avoid restrictive, a priori filtering of genes by exploiting the gene-set property of mutual exclusivity, expressed in terms of the functional impact scores of mutations, rather than in terms of simple binary mutation calls. Applying OMEN to a benchmark dataset derived from TCGA illustrates how OMEN is able to robustly identify driver genes and modules of driver genes as proxies of driver pathways. AVAILABILITY AND IMPLEMENTATION: The source code is freely available for download at www.github.com/DriesVanDaele/OMEN. The dataset is archived at https://doi.org/10.5281/zenodo.6419097 and the code at https://doi.org/10.5281/zenodo.6419764. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Manufacturing companies rely on technical drawings to develop new designs or adapt designs to customer preferences. The database of historical and novel technical drawings thus represents the knowledge that is core to their operations. With current methods, however, utilizing these drawings is mostly a manual and time consuming effort. In this work, we present a software tool that knows how to interpret various parts of the drawing and can translate this information to allow for automatic reasoning and machine learning on top of such a large database of technical drawings. For example, to find erroneous designs, to learn about patterns present in successful designs, etc. To achieve this, we propose a method that automatically learns a parser capable of interpreting technical drawings, using only limited expert interaction. The proposed method makes use of both neural methods and symbolic methods. Neural methods to interpret visual images and recognize parts of two-dimensional drawings. Symbolic methods to deal with the relational structure and understand the data encapsulated in complex tables present in the technical drawing. Furthermore, the output can be used, for example, to build a similarity based search algorithm. We showcase one deployed tool that is used to help engineers find relevant, previous designs more easily as they can now query the database using a partial design instead of through limited and tedious keyword searches. A partial design can be a part of the two-dimensional drawing, part of a table, part of the contained textual information, or combinations thereof.