Benchmarking network propagation methods for disease gene identification

In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genesPeer ReviewedPostprint (published version

Tests of amounts and times of application of nitrogen and of sequential sprays of aphicide and fungicides on winter wheat, following either beans or wheat, and the effects of take-all (Gaeumannomyces graminis var. tritici ), on two varieties at Saxmundham, Suffolk 1980-3

RESP-9258 and RESP-924

Digital PCR analysis of circulating tumor DNA: a biomarker for chondrosarcoma diagnosis, prognostication, and residual disease detection

Conventional chondrosarcoma is the most common primary bone tumor in adults. Prognosis corresponds with tumor grade but remains variable, especially for individuals with grade (G) II disease. There are currently no biomarkers available for monitoring or prognostication of chondrosarcoma. Circulating tumor DNA (ctDNA) has recently emerged as a promising biomarker for a broad range of tumor types. To date, little has been done to study the presence of ctDNA and its potential utility in the management of sarcomas, including chondrosarcoma. In this study, we have assessed ctDNA levels in a cohort of 71 patients, 32 with sarcoma, including 29 individuals with central chondrosarcoma (CS) and 39 with locally aggressive and benign bone and soft tissue tumors, using digital PCR. In patients with CS, ctDNA was detected in pretreatment samples in 14/29 patients, which showed clear correlation with tumor grade as demonstrated by the detection of ctDNA in all patients with GIII and dedifferentiated disease (n = 6) and in 8/17 patients with GII disease, but never associated with GI CS. Notably detection of ctDNA preoperatively in GII disease was associated with a poor outcome. A total of 14 patients with CS had ctDNA levels assessed at multiple time points and in most patients there was a clear reduction following surgical removal. This research lays the foundation for larger studies to assess the utility of ctDNA for chondrosarcoma diagnosis, prognostication, early detection of residual disease and monitoring disease progression

Enhancing the early student experience

This paper is concerned with identifying how the early student experience can be enhanced in order to improve levels of student retention and achievement. The early student experience is the focus of this project as the literature has consistently declared the first year to be the most critical in shaping persistence decisions. Programme managers of courses with high and low retention rates have been interviewed to identify activities that appear to be associated with good retention rates. The results show that there are similarities in the way programmes with high retention are run, with these features not being prevalent on programmes with low retention. Recommendations of activities that appear likely to enhance the early student experience are provided