1,720,993 research outputs found
Using Robust Rank Aggregation for Prioritising Autoimmune Targets on Protein Microarrays
Get PDFAutoimmuunhaigused on tänapäeva maailmas väga sagedased. Üha enam ja enam haigusi on seotud autoimmuunsete protsessidega. Autoimmuunreaktsioon on protsess, mille käigus immuunsüsteem toodab antikehasid (autoantikehad) organismi enda rakkude vastu. Autoimmuunhaiguste põhjused ja mehhanismid on aga veel selgeks tegemata. Üheks võimaluseks, kuidas autoimmuunhaigusi õppida on välja selgitada, miks kindlad rakud ja iseäranis just valgud on autoantikehade märklauaks. Selle eesmärgi saavutamiseks on välja töötatud mitmesuguseid tehnoloogiaid, kuhu kuuluvad ka valgukiibid. See tehnoloogia võimaldab hinnata autoantikehade kogust patsiendi seerumis 9000 unikaalse inimese valgu vastu. Seega, rakendades andmeanalüüsi meetodeid on bioinformaatikud võimelised tuvastama autoantikehade märklaudvalke. Teades neid valke, saavad bioloogid läbi viia edasisi katseid ning formuleerida uusi hüpoteese autoimmuunhaiguste mehhanismide ja esinemise kohta. Traditsioonilised andmeanalüüsi meetodid keskenduvad ainult selliste valkude leidmisele, mis erinevad kõige kindlamalt tervete ja patsientide grupi vahel. Need meetodid aga jätavad kõrvale fakti, et märklaudvalkude repertuaar võib patsientide vahel oluliselt erineda. Seega võib isegi üksikjuhtum sisaldada olulist informatsiooni haiguse mehhanismide mõistmisel. Käesolevas lõputöös pakume välja, et Robust Rank Aggregation (RRA) algoritmi saab kasutada adaptiivse meetodina leidmaks reaktiivsete valkude (märklaudvalkude) laia repertuaari. Me võrdlesime klassikaliste analüüsimeetodite otstarbekust ja efektiivsust RRA-ga nii sünteetilistel kui ka pärisandmetel. Katsed sünteetilise andmehulgaga ehk andmehulgaga, mille puhul on reaktiivsed valgud teada näitavad, et RRA ületab teisi meetodeid olles samal ajal vähem mõjutatud “mürast”. Rakendades RRA-d pärisandmetel ning viies läbi rikastusanalüüsi iga meetodi kohta saadud reaktiivsete valkude listidega, saime me sarnase arvu valke, mis olid bioloogilise ja immuunvastusega seotud klassides üleesindatud.Autoimmune diseases are very common in the modern world. More and more diseases associated with an autoimmune process. Autoimmune reaction is a process in which the immune system produces antibodies (autoantibodies) that attack organism’s own cells. Causes and mechanisms of autoimmune diseases are yet to be understood. One of the ways to study autoimmunity is to explore reasons why certain cells and particularly proteins were attacked by autoantibodies. To achieve this, many technologies have been developed and one of which is Protein microarray. This technology allows estimating the amount of autoantibodies in patient serum against 9000 unique human proteins. Consequently, applying methods of data analysis on this data, bioinformaticians might be able to identify proteins that attract prevalent amount of autoantibodies. Knowing these proteins, biologists could conduct experiments and formulate new hypotheses about mechanisms of work and appearance of autoimmune diseases. Common data analysis methods focused on how to select only the most reliably differing proteins between healthy and diseased groups. Moreover, ignoring the fact that in the case of an autoimmune disease - the repertoire of the affected proteins can differ greatly between patients. So even single cases of high protein reactivity may carry important information for understanding the mechanisms of disease. In this thesis, we propose to apply Robust Rank Aggregation algorithm as an adaptive method to identify a wide repertoire of reactive proteins. We compared expediency and effectiveness of the classical methods of analysis, method recently applied by biologists and RRA on synthetic and real data. Experiments on synthetic data sets with known reactive proteins show that RRA outperforms these methods while also being more robust to incorporated noise. Applying RRA on real data and conducting an enrichment analysis on lists of reactive proteins for each method, we got comparable numbers of proteins overrepresented in the classes associated with biological and immune responses
Computer vision meets microbiology: deep learning algorithms for classifying cell treatments in microscopy images
Get PDFCell classification is one of the most complex challenges in cellular research that has
significant importance to personalised medicine, cancer diagnostics and disease prevention.
The accurate classification of cells based on their unique characteristics provides valuable
insights into a patient's health status and in guiding treatment decisions. Thanks to recent
technological advancements, cellular research has experienced significant progress in the use
of deep learning and has become a valuable tool for tackling complicated tasks such as cell
classification. In this study, we explored the capability of state-of-the-art deep learning
models such as ResNet, ViT and Swin Transformer to automatically classify brightfield and
fluorescent microscopy images across single and multiple channels into four cell treatments:
Palbociclib, MLN8237, AZD1152, and CYC116. The results have revealed that Swin
Transformer surpasses the other models for cell treatment classification on multi-channel
fluorescent and brightfield images, achieving the highest accuracy of 86% and 59%,
correspondingly. However, the highest accuracy achieved on single-channel brightfield
images was 61%, using the ResNet-50 model. The previous research has shown that
combining multiple channels yields better performance which necessitates further
investigation into the capacity of deep learning models for automating the cell treatment
classification of single- and multi-channel brightfield microscopy images
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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