1,720,974 research outputs found
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
New Faculty Talks
No full textModerator: Dr. Michael Green, Chair and Professor, Molecular, Cell and Cancer Biology Presenters: Dr. Johanna Seddon, Professor, Ophthalmology and Visual Sciences: “Macular Degeneration epidemiology: nature-nurture, lifestyle factors, genetic risk, and gene-environment interactions: clues to therapies” Dr. Sohye Kim, Assistant Professor, Psychiatry: “Neural markers of social engagement in the first year of life” Dr. Andres Colubri, Assistant Professor, Microbiology and Physiological Systems: "ORAN: A meta-modeling platform to drive real-life and online outbreak simulations
Data-Driven Modeling for Infectious Disease Prediction: Navigating Irregularities in Resource-Limited Settings
No full textInfectious diseases continue to impose heavy burdens in low- and middle-income countries, where prediction and response are hindered by scarce, irregular data. Clinical and surveillance datasets in these settings are typically small, incomplete, imbalanced, and uncertain—conditions that challenge conventional machine learning models. Yet it is precisely under such imperfect circumstances that predictive modeling can save lives. This dissertation advances a data-diagnostic-first framework for infectious disease prediction that systematically addresses three pervasive data irregularities—class imbalance, missingness, and uncertainty—through methodological innovation and empirical validation. First, I introduce SMARTSMOTE (Structure- and Manifold-Aware Technique for Synthetic Minority Oversampling), a resampling method developed after evaluating deficiencies in existing techniques that distort data geometry or amplify noise. By preserving manifold structure and local variance, SMARTSMOTE improves minority recall and interpretability for rare infections such as Borrelia and Lassa fever. Next, I present MICE-GAIN, a hybrid imputation approach combining multiple chained equations and generative adversarial inference to recover missing clinical variables in resource-limited datasets. Sensitivity analyses on Lassa fever data reveal that imputation choice substantially influences downstream reliability and calibration, highlighting the importance of aligning data recovery with model interpretability. Finally, I incorporate Bayesian and variance-based modeling to quantify aleatoric and epistemic uncertainty, revealing how preprocessing reshapes predictive confidence. Together, these components form an irregularity-aware modeling pipeline validated across pathogen datasets in Senegal, Nigeria, and the United States. The framework enhances predictive accuracy, calibration, and interpretability under data scarcity, advancing a paradigm that treats data irregularities not as obstacles but as diagnostic signals for building equitable and trustworthy AI in global health.Interdisciplinary Graduate Program1 year2026-11-1
A Prognostic Model to Predict Survival in Children with Ebola Virus Disease
Get PDFRepeated outbreaks of Ebola Virus Disease (EVD) in low-resource settings emphasize the importance of evidence-based guidelines to direct treatment. Previous research has shown that EVD causes high case fatality rates (CFRs) in young children, yet there are limited data focusing on pediatric patients. Here we present a prognostic model to predict mortality in children who are Ebola-positive using information available during the first 48 hours after admission to the treatment center. A logistic regression model was trained on triage data from the Ebola Data Platform, a repository of retrospective patient data compiled from actors that responded to the West African EVD outbreak from 2014-2016. Patients <18 years of age were included in the analysis (N=579) and the CFR was 40%. Overall 13% of data were missing, and multiple imputation was used to estimate missing values. Variable selection using elastic net regularization selected age, CT value, bleeding, breathlessness, bone or muscle pain, anorexia, swallowing problems, and diarrhea as predictors. Bootstrap validation yielded an optimism-corrected area under the curve (AUC) of 0.75 (95% CI: 0.71-0.79). The model was externally validated using data from the current EVD outbreak in the Democratic Republic of the Congo (DRC). While the model’s discriminative ability on the DRC data was similar (AUC=0.75, 95% CI: 0.63-0.87) to the training data, calibration was poor. We recalibrated the model by re-estimating the intercept and slope, and further improved model performance by including aspartate aminotransferase (AST) as a biomarker. The updated model with AST as an added predictor has an AUC of 0.90 (95% CI: 0.77-1). These preliminary results are encouraging but should be interpreted with caution because of limited availability of AST values in the validation data (n=25). The prognostic model described here has promising potential for use in a clinical setting and will continue to be validated as more data becomes available. Future efforts will focus on integrating the validated model into mHealth tools to aid clinicians in making informed, data-driven decisions about patient care.Bioinformatics and Computational Biolog
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
ebola: Dataset v1.2
No full text<p>This data comprises a total of 213 cases evaluated for Ebola virus infection at the Kenema Government Hospital in Sierra Leone between May 25 and June 18, 2014. Details in “Clinical Illness and Outcomes in Patients with Ebola in Sierra Leone”, by John S. Schieffelin, et al.</p>
<p>Available are raw files (Excel, VCF formats), Mirador project, and single CSV file.</p>
<p>Zenodo record (DOI): https://zenodo.org/record/13163</p>
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
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