1,721,143 research outputs found
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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Discovering Molecular Patterns with Therapeutic Implications in Large-Cohort Heterogeneous Cross-Cancer Data
Full text linkRecent advances in high-throughput genomic technologies and high-performance computing have propelled the science of computational genomics into a new era and launched the field of precision medicine. Computational genomics is now an integral part of biomedical research and genomic testing is routinely performed in clinical settings. In the field of cancer informatics, the integration of genomics has led to invaluable insights and discoveries. We study cancers in order to better understand tumorigenesis and disease progression. This understanding can, in turn, inform and guide therapeutic decisions and suggest directions for drug development and repositioning. The ultimate goal of cancer precision medicine is to sequence and analyze every patients tumor in order to provide the most effective and least toxic treatment.Various experimental platforms are available for collection of different perspectives or views of the cell state, which help us characterize and understand molecular signals driving the cell phenotype. We collectively refer to these views as ’omics’ data. While vast amounts of ’omics’ data are being collected from tumor samples at an accelerating rate, few resources exist to aid biologists and clinicians in identifying trends in these data, finding connections within and between cancer subtypes, and matching patients to previously studied patient groups to infer therapeutic implications. In our analysis we also utilize bioinformatics methods that manipulate, transform, and integrated these views to derive new views of the cell. In my doctoral thesis, I present my work developing new tools and methods to aid the scientific community in understand- ing and interpreting cancer biology (Chapter 2). I also present my work applying such methods to contribute to cancer subtype-specific analyses as part of various projects and collaborations during my doctoral work (Chapter 3).Finally, I describe my work and contributions to the field of personalized medicine in pediatric cancer. While similar in some ways to adult cancers, pediatric cancers differ dramatically from their adult counterparts on a molecular level. For ex- ample, pediatric tumors generally have fewer genomic alterations than adult tumors. Further,childhood cancers are rarer than adult cancers and thus more difficult to study due to a lack of sufficiently large patient cohorts. While some clinics now regularly sequence pediatric tumors for bioinformatic analysis, the sequencing of patient genomes in the clinic is only beginning to impact patient care. Most computational methods for detecting differentially expressed genes are designed for analyzing patient cohorts in research settings and are thus unsuitable for interpreting RNA sequencing data from a single patient. Further, analyzing individuals genomic data leads to actionable treatment options in only fifteen percent of all childhood cancer cases. This is because pediatric cancers are often not driven by non-hereditary genomic changes, and any genomic aberrations that do exist may not be targetable by existing drugs. More sophisticated informatics tools and methods are needed in the field of personalized medicine. To this end, I describe my work developing methods for single-patient analyses in pediatric cancer (Chapter 4). While my methods were developed for pediatric cancers, they may also be used to analyze adult tumors
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Transcriptional Signatures of the Tumor and the Tumor Microenvironment Predict Cancer Patient Outcomes.
Full text linkPredicting the most effective cancer therapy for patients is a challenging yet very important task. In my doctoral thesis, I describe new insights gained from using transcriptional signatures from gene expression data of tumors and the tumor microenvironment. Out of different multi-omics data types, gene expression is found to be the most useful in predicting cancer drug sensitivity in a data set of cancer cell lines. Gene expression data can also be used to predict the presence of cancer driver events, genetic abnormalities responsible for tumor growth and progression. I describe the detection of rare genomic driver events found by association with known driver events using transcriptional signatures.Cancers are traditionally classified into types and subtypes by the organ- and cell-of-origin. However, more and more cancer subtypes are now being defined on a molecular basis using for example gene expression or mutation data. I perform a meta-analysis of molecular subtype classifiers for 26 different cancer cohorts that demonstrates which aspects of the input samples and input data are important to build an accurate molecular subtype classifier. In advanced prostate cancer, I use transcriptional signatures to reliably classify samples into subtypes. The gene expression data, in combination with histological review, is able to define the most at-risk patients in this cohort. However, I show that the classification of cancers into distinct subtypes is not applicable to all samples in this cohort, because they exist on a continuous spectrum between the subtypes - a finding that I was able to recapitulate in a study of lung cancer samples. I define and describe this continuum between subtypes of advanced prostate cancer using gene expression data.The tumor microenvironment, the normal cells that mix with cancer cells to form a tumor, plays an important role in cancer progression and treatment response. I built a landscape of about 10,000 cancer samples from immune cell infiltration estimated by deconvolution of gene expression profiles. However, immune cells are not the only cell types in the tumor microenvironment. I present a comprehensive deconvolution analysis of tumor patient samples using a cell type library defined from single-cell RNA sequencing data. These cell type estimates enable the detection of a pan-cancer high-risk sample group that is not detected by traditional gene expression analysis
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Sample-Specific Cancer Pathway Prediction From Genomic, Transcriptomic and Phosphoproteomic Data
Full text linkCancer phenotypes such as invasion, evasion of programmed cell death, and rapid growth, arise from the complex interactions of genes, proteins and extra-cellular environments. Understanding how selective alterations in the genome convert healthy cells to cancer is a critical step in developing new targeted and combination therapies. Current technology allows for detailed measurement of both genomic state as well as phenotype, through measurement of gene expression, chromatin state and protein activation. I present a method, Tied Diffusion through Interacting Events (TieDIE), that uses a “heat diffusion” model of information transfer to find pathways linking key genomic alterations to phenotypic effects, using high-throughput data collected from cohorts of cancer patients. Applying this method to four large data sets developed by The Cancer Genome Atlas (TCGA), I found key genes and interactions linking mutations related to histone modification and protein kinase signaling to gene-expression signatures of growth and proliferation. In a TCGA study of thyroid carcinoma, TieDIE found key proteins that modulate oncogenic signaling from mutant BRAF and RAS proteins to downstream MEK and ERK pathways, including the kinase suppressor of ras 1 scaffold protein.TieDIE was next applied to a study of prostate cancer that included detailed measurements of the phosphpoproteome. With collaborators at UCLA, we used tissue from lethal, metastatic castration-resistant prostate cancer (CRPC) patients obtained from rapid autopsy to evaluate diverse genomic, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using TieDIE, I integrated transcriptional, genomic and phosphoproteomics datasets to reveal a “map” of activated kinases and signaling pathways in CRPC. In contrast to single-dataset analyses, I show this integrative approach provides a more comprehensive and detailed look at metastatic signaling, and is generally useful in combining diverse datasets with only partially overlapping samples. Patient-specific network models were created by intersecting each sample’s data and protein activity predictions with the CRPC signaling “map.” These models reveal a hierarchy of the top kinase targets for each patient analyzed, and the corresponding therapeutic intervention, allowing for the construction of feasible strategies for patients with high activities in multiple kinases
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