41 research outputs found
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DNA methylation-based classification of central nervous system tumours.
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology
Online chemical adsorption studies of Hg, Tl, and Pb on SiO2 and Au surfaces in preparation for chemical investigations on Cn, Nh, and Fl at TASCA
Online gas-solid adsorption studies with single-atom quantities of Hg, Tl, and Pb, the lighter homologs of the superheavy elements (SHE) copernicium (Cn, Z =112), nihonium (Nh, Z =113), and flerovium (Fl, Z =114), were carried out using short-lived radioisotopes. The interaction with Au and SiO 2 surfaces was studied and the overall chemical yield was determined. Suitable radioisotopes were produced in fusion-evaporation reactions, isolated in the gas-filled recoil separator TASCA, and flushed rapidly to an adjacent setup of two gas chromatography detector arrays covered with SiO 2 (first array) and Au (second array). While Tl and Pb adsorbed on the SiO 2 surface, Hg interacts only weakly and reached the Au-covered array. Our results contribute to elucidating the influence of relativistic effects on chemical properties of the heaviest elements by providing experimental data on these lighter homologs
DNA methylation-based classification and grading system for meningioma:a multicentre, retrospective analysis
Large expert-curated database for benchmarking document similarity detection in biomedical literature search
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe