1,720,964 research outputs found
Role of mobile genetic elements in the global network of bacterial horizontal gene transfer
Full text linkMany bacteria can exchange genetic material through horizontal gene transfer (HGT) mediated by plasmids and plasmid-borne transposable elements. One grave consequence of this exchange is the rapid spread of antibiotic resistance determinants among bacterial communities across the world. In this thesis, I make use of large datasets of publicly available bacterial genomes and various analytical approaches to improve our understanding of the nature and the impact of HGT at a global scale. In the first part, I study the population structure and dynamics of over 10,000 bacterial plasmids. By reconstructing and analysing a network of plasmids based on their shared k-mer content, I was able to sort them into biologically meaningful clusters. This network-based analysis allowed me to make further inferences into global network of HGT and opened up prospect for a natural and exhaustive classification framework of bacterial plasmids. The second part focuses on global spreading of blaNDM – an important antibiotic resistance gene. To this end, I compiled a dataset of over 6000 bacterial genomes harbouring this element and developed a novel computational approach to track structural variants surrounding blaNDM across bacterial genomes. This facilitated identification of prevalent genomic contexts of blaNDM and reconstruction of key mobile genetic elements and events which led to its global dissemination. Taken together, my results highlight transposable elements as the main drivers of HGT at broad phylogenetic and geographical scales with plasmid exchange being much more spatially restricted due to the adaptation to specific bacterial hosts and evolutionary pressures
Chosen machine learning methods and their application in molecular biology
Full text linkStrojno učenje je područje računalnih znanosti koje kroz razvoj algoritama omogućava računalima da nauče, modeliraju i razumiju kompleksne skupove podataka. Razni algoritmi strojnog učenja “uče se” izvršavati određeni zadatak na podacima za trening, a naučeno se primjenjuje na novo unesenim podacima. Strojevi programirani algoritmom postaju sve bolji u obavljanju određenih zadataka što imaju više iskustva. Postoje dvije temeljne podjele algoritama za strojno učenje. Algoritmi s nadzorom pokušat će povezati dva tipa podataka: prediktore i odgovor, odnosno predvidjeti odgovor za novo unesene prediktore. Algoritmi bez nadzora kao konačan cilj nastoji opisati i grupirati unesene podatke. Druga podjela algoritama je na regresijske i klasifikacijske. Algoritmi kojima se razmatraju kvantitativne varijable nazivaju se regresijski algoritmi, a oni koji proučavaju kvalitativne varijable nazivaju se klasifikacijski. U radu su opisane tri metode strojnog učenja, te su za svaku metodu predstavljeni neki primjeri primjene u molekularnoj biologiji. Random forests prva je od predstavljenih metoda s nadzorom koja se temelji na izgradnji šume stabala odluke. Konačan predviđeni odgovor dobiva se usrednjavanjem odgovora svih stabala. Metoda potpornih vektora za svoj rad koristi kernele, a cilj joj je pronaći optimalnu razdvajajuću hiperravninu i tako što točnije podijeliti opservacije na dvije ili više podgrupa. Posljednji je algoritam K-srednjih vrijednosti koji će unesene podatke pokušati podijeliti u unaprijed određeni broj podgrupa na temelju međusobne sličnosti. U zaključku nastojim opisati širu sliku područja strojnog učenja, pružiti neke dodatne zanimljive informacije, ideje i metode, te korisne savjete. Također, ističem važnost pojedinih koraka koji prethode samoj primjeni odabrane metode.Machine learning is a field of computer science which enables computers to learn how to manipulate and understand complex datasets through algorithm development. Various types of machine learning algorithms are trained on training datasets to apply specific tasks on new input data. Programmed machines gain experience with time which makes them more reliable and successful. There are two major classifications of machine learning algorithms. Supervised algorithms are trying to relate two types of data: predictors and response. They will estimate the response based on input predictor. On the other hand, unsupervised algorithms are used to group and describe the given dataset. Second classification is considering regression and classification algorithms. Algorithms that are used for computations with quantitative variables are called regression algorithms, and those manipulating qualitative variables are classification algorithms. In this bachelor’s thesis three machine learning methods are described. For each method examples are given with the application in molecular biology. Random forests is the first supervised method presented. It is based on constructing a forest of decision trees. Final response is estimated by averaging the response of all the trees. Support vector machine is using kernel functions to find the maximum margin hyperplane in order to divide the given dataset into two distinct groups. Last, K-means clustering algorithm will tend to divide the given dataset into predefined K number of groups based on similarity of the data. In the conclusion an overview of the machine learning field is given along with the additional information, ideas, methods and advices. Moreover, the importance of the steps that precede the algorithm implementation are highlighted and explained
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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