1,720,974 research outputs found
Automatic Tumour Typing based on Patterns of Somatic Passenger Mutations
No full textIn cancer, a tumour’s cell of origin is the strongest determinant of its clinical behaviour. While cell of origin is typically clear at the time of diagnosis, 3-5% of cancer patients present with a metastatic tumour and no obvious corresponding primary tumour. Despite advances in molecular testing, imaging, and pathology, the primary tumour site cannot be inferred in the majority of these cases. Recent large- scale analysis of cancer genomes has uncovered strong associations between cancer type and somatic mutations, prompting the use of somatic mutations as a tool for identifying cancer type. While existing approaches have attempted to use cancer-associated mutations, which may be more common in specific cancer types to infer the primary tumour type from the metastatic tissue, these methods have had only limited success. A more promising alternative is to use the association between patterns of somatic passenger mutations and cancer type, by exploiting the relationships between both regional mutation density and cancer type, and mutational processes and cancer type. Somatic point mutations accu- mulate in regions of closed chromatin, and so mutation density provides information about chromatin state, which in turn offers hints about the underlying cell type. As some mutational processes are highly cell-type specific, mutational processes also provide clues about cancer type. In this thesis, I describe a number of deep learning systems for automatic tumour typing based on patterns of somatic passenger mutations. I then address challenges for translating the classifier into clinical scenarios through the use of multiple algorithmic improvements. First, I make use of modern advancements in deep learning to extend the classifier to accurately discriminate between 29 cancer types. I then use a number of sta- tistical methods for assessing the uncertainty in the model’s predictions, and for improving uncertainty quantification. Finally, I make use of information theoretic metrics to use the model’s predictive uncer- tainty to automatically detect cancer samples that come from rare cancer types that the model was not trained to classify. These studies demonstrate the utility of passenger mutations as a tool for identifying cancer type, and address challenges for translating the deep learning classifier into clinical settings.Ph.D.2022-06-29 00:00:0
Author Correction: A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
No full textAuthor Correction: A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Sex differences in oncogenic mutational processes
No full textAbstract Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research.Gouvernement du Canada | Instituts de Recherche en Santé du Canada | CIHR Skin Research Training Centre https://doi.org/10.13039/501100007202U.S. Department of Health & Human Services | NIH | National Cancer Institute https://doi.org/10.13039/100000054Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada https://doi.org/10.13039/501100002790Genome Canada https://doi.org/10.13039/100008762Canada Foundation for Innovation https://doi.org/10.13039/501100000196Terry Fox Research Institute https://doi.org/10.13039/50110000437
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