1,721,009 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
Reconstructing Complex Cancer Evolutionary Histories from Multiple Bulk DNA Samples
No full textCancers are composed of genetically distinct subpopulations of malignant cells. By sequencing DNA from cancer tissue samples, we can characterize the somatic mutations specific to each population and build clone trees describing the evolutionary relationships between these populations. These trees reveal critical points in disease development and inform treatment.
This thesis describes Pairtree, which is a new method for constructing clone trees using DNA sequencing data from one or more bulk samples of an individual cancer. It uses Bayesian inference to compute posterior distributions over the evolutionary relationships between every pair of identified subpopulations, then uses these distributions in a Markov Chain Monte Carlo algorithm to perform efficient inference of the posterior distribution over clone trees. Pairtree also uses the pairwise relationships to detect mutations that violate the infinite sites assumption. Unlike previous methods, Pairtree can perform clone tree reconstructions using as many as 100 samples per cancer that reveal 30 or more cell subpopulations. On simulated data, Pairtree is the only method whose performance reliably improves when provided with additional bulk samples from a cancer. On 14 B-progenitor acute lymphoblastic leukemias with up to 90 samples from each cancer, Pairtree was the only method that could reproduce or improve upon expert-derived clone tree reconstructions. Beyond describing Pairtree and evaluating it relative to previous methods, we develop extensions for profiling the degree of uncertainty that exists in its solution set, quantifying the degree of heterogeneity that occurs in different cancer samples, and deciding whether two cancer samples are genetically similar. By scaling to more challenging problems, Pairtree supports new biomedical research applications that can improve our understanding of the natural history of cancer, as well as better illustrate the interplay between cancer, host, and therapeutic interventions.Ph.D
Inferring RNA Sequence Specificities from Protein Sequences to Characterize Post-transcriptional Regulation in Eukaryotes
Full text linkMost RNA-binding proteins (RBPs) find their targets through unique binding preferences towards specific RNA sequence or RNA sequence-structure patterns, called specificities. Recently, we used RNAcompete, an in vitro binding assay, to measure the RNA sequence specificities of 174 RBPs. Combined with previous measurements, we established the largest collection of RBP sequence specificities to date, containing 381 RBPs from 33 eukaryotes, from protists to humans. RNA sequence specificities are nearly always conserved if RBP sequences share more than 70% identity in their binding regions (70%-rule). However, only half of the RBPs sharing between 30% and 70% sequence identity recognize similar RNA, limiting the confidence in predictions from sequence identity.
To increase the number of RBPs with a confidently inferred specificity from our measured data, I developed a computational method, called joint protein-ligand embedding (JPLE), which jointly embeds amino acid 5-mers and RNA sequence specificities into a joint latent space. The joint latent embedding of an RBP sequence can be approximated from protein sequence features alone and enables reconstructions of RNA sequence specificity, prediction of RNA binding similarity, and identification of important binding regions in the protein sequence. JPLE doubles the number of RBPs with confidently inferred RNA sequence specificities compared to predictions with the 70%-rule.
I embed RBPs from 690 eukaryotes in JPLE’s latent space, confidently reconstruct specificities for 29,000 RBPs, and cluster RNA sequence specificity groups (RSSGs). I use these RSSGs to estimate the number of distinct RSSGs for all eukaryotic RBPs with RRM and KH domains. I confirm that RRM containing RBPs recognize highly diverse sequences, covering every possible 7-mer, while KH containing RBPs recognize only 40% of all possible 7-mer sequences. Moreover, I determine the last common ancestor of the RBPs in an RSSG and derive the evolutionary rate with which new RNA sequence specificities evolve in different eukaryotic clades. Lastly, to demonstrate the utility of this new resource to investigate post-transcriptional regulation, I combine 101 inferred RNA sequence specificities from Arabidopsis thaliana with RNA-seq from 69 plant tissues and I identify RBPs that regulate mRNA stability through interactions with the 3’UTR.Ph.D
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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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