Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the ‘Disease Module Identification DREAM Challenge’, an open competition to comprehensively assess module identification methods across diverse protein–protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

The CRISPR-Cas9 system provides unprecedented genome editing capabilities. However, off-target effects lead to sub-optimal usage and additionally are a bottleneck in the development of therapeutic uses. Herein, we introduce the first machine learning-based approach to off-target prediction, yielding a state-of-the-art model for CRISPR-Cas9 that outperforms all other guide design services. Our approach, Elevation, consists of two interdependent machine learning models—one for scoring individual guide-target pairs, and another which aggregates these guide-target scores into a single, overall summary guide score. Through systematic investigation, we demonstrate that Elevation performs substantially better than competing approaches on both tasks. Additionally, we are the first to systematically evaluate approaches on the guide summary score problem; we show that the most widely-used method performs no better than random at times, whereas Elevation consistently outperformed it, sometimes by an order of magnitude. We also introduce an evaluation method that balances errors between active and inactive guides, thereby encapsulating a range of practical use cases; Elevation is consistently superior to other methods across the entire range. Finally, because of the large scale and computational demands of off-target prediction, we have developed a cloud-based service for quick retrieval. This service provides end-to-end guide design by also incorporating our previously reported on-target model, Azimuth. (https://crispr.ml:please treat this web site as confidential until publication).

Summary We define a disease module as a partition of a molecular network whose components are jointly associated with one or several diseases or risk factors thereof. Identification of such modules, across different types of networks, has great potential for elucidating disease mechanisms and establishing new powerful biomarkers. To this end, we launched the ‘Disease Module Identification (DMI) DREAM Challenge’, a community effort to build and evaluate unsupervised molecular network modularization algorithms. Here, we present MONET, a toolbox providing easy and unified access to the three top-performing methods from the DMI DREAM Challenge for the bioinformatics community. Availability and implementation MONET is a command line tool for Linux, based on Docker and Singularity containers; the core algorithms were written in R, Python, Ada and C++. It is freely available for download at https://github.com/BergmannLab/MONET.git. Supplementary information Supplementary data are available at Bioinformatics online.

In biomedical applications of machine learning, relevant information often has a rich structure that is not easily encoded as real-valued predictors. Examples of such data include DNA or RNA sequences, gene sets or pathways, gene interaction or coexpression networks, ontologies, and phylogenetic trees. We highlight recent examples of machine learning models that use structure to constrain model architecture or incorporate structured data into model training. For machine learning in biomedicine, where sample size is limited and model interpretability is critical, incorporating prior knowledge in the form of structured data can be particularly useful. The area of research would bene t from performant open source implementations and independent benchmarking e orts.Structure of ontology, phylogeny, etc. Labels or cluster assignments Tabular dataSchematic showing the main categories of models incorporating structured biological data covered in this review. The rst panel shows an example of a model operating on raw sequence data, the second panel shows a model in which dimension reduction is constrained by the connections in a gene network, and the third panel shows a neural network with structure constrained by a phylogeny or ontology.