RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with poorly defined aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the peripheral blood immune cell landscape of early, drug naive RA, we performed comprehensive clinical and molecular profiling of 267 RA patients and 52 healthy vaccine recipients for up to 18 months to establish a high quality sample biobank including plasma, serum, peripheral blood cells, urine, genomic DNA, RNA from whole blood, lymphocyte and monocyte subsets. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and autoantibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource offering insights into immune-mediated disease pathogenesis, progression and therapeutic response, ultimately contributing to the development and application of targeted therapies for RA.</p

iMauve: A fast incremental model tree learner

This abstract was presented as a poster at the 17th International Conference on Discovery Science.status: publishe

Sensitivity analysis of search-space dimensionality on recent multi-objective evolutionary algorithms

Many methods for multi-objective optimisation exist, and there are multiple studies in which their performance is compared in terms of a wide range of evaluation metrics. Usually, these studies compare the end result of the optimisation process on given benchmarks; they do not evaluate how fast this end result is obtained, nor how properties of the benchmarks affect these results. In this paper, we investigate how the search space dimensionality of optimisation problems affects the behaviour of different methods, not only in terms of the end result but also in terms of how fast it is achieved. We compared two particle-swarm based optimisers, an elitist evolutionary algorithm and a scatter search algorithm. Our results show that while the PSO-based methods generally converge faster or equally fast compared to the others, they found a less diverse set of solutions.status: publishe

Multi-objective optimization with surrogate trees

Multi-objective optimization problems are usually solved with genetic algorithms when the objective functions are cheap to compute, or with surrogate-based optimizers otherwise. In the latter case, the objective functions are modeled with powerful non-linear model learners such as Gaussian Processes or Support Vector Machines, for which the training time can be prohibitively large when dealing with optimization problems with moderately expensive objective functions. In this paper, we investigate the use of model trees as an alternative kind of model, providing a good compromise between high expressiveness and low training time. We propose a fast surrogate-based optimizer exploiting the structure of model trees for candidate selection. The empirical results show the promise of the approach for problems on which classical surrogate-based optimizers are painfully slow.status: publishe

RADAR-base/RADAR-Gateway: radar-gateway 0.7.0

&lt;p&gt;Changes since version 0.6.0:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Use coroutines for a larger subset of functions&lt;/li&gt; &lt;li&gt;Updated authentication handling to radar-auth 2.1.0&lt;/li&gt; &lt;li&gt;Updated radar-commons to 1.1.1 including plugin handling&lt;/li&gt; &lt;li&gt;More consistent Avro context handling while parsing&lt;/li&gt; &lt;/ul&gt

Data and knowledge management in translational research: implementation of the eTRIKS platform for the IMI OncoTrack consortium

For large international research consortia, such as those funded by the European Union’s Horizon 2020 programme or the Innovative Medicines Initiative, good data coordination practices and tools are essential for the successful collection, organization and analysis of the resulting data. Research consortia are attempting ever more ambitious science to better understand disease, by leveraging technologies such as whole genome sequencing, proteomics, patient-derived biological models and computer-based systems biology simulations

Erratum: The RA-MAP Consortium: a working model for academia–industry collaboration

This corrects the article DOI: 10.1038/nrrheum.2017.200

The RA-MAP Consortium: A working model for academia-industry collaboration

Collaboration can be challenging; nevertheless, the emerging successes of large, multi-partner, multi-national cooperatives and research networks in the biomedical sector have sustained the appetite of academics and industry partners for developing and fostering new research consortia. This model has percolated down to national funding agencies across the globe, leading to funding for projects that aim to realise the true potential of genomic medicine in the 21st century and to reap the rewards of 'big data'. In this Perspectives article, the experiences of the RA-MAP consortium, a group of more than 140 individuals affiliated with 21 academic and industry organizations that are focused on making genomic medicine in rheumatoid arthritis a reality are described. The challenges of multi-partner collaboration in the UK are highlighted and wide-ranging solutions are offered that might benefit large research consortia around the world