Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

A meta-analysis of the effectiveness of intelligent tutoring systems on college students' academic learning

© 2013 American Psychological Association.This meta-analysis synthesizes research on the effectiveness of intelligent tutoring systems (ITS) for college students. Thirty-five reports were found containing 39 studies assessing the effectiveness of 22 types of ITS in higher education settings. Most frequently studied were AutoTutor, Assessment and Learning in Knowledge Spaces, eXtended Tutor-Expert System, and Web Interface for Statistics Education. Major findings include (a) Overall, ITS had a moderate positive effect on college students' academic learning (g = .32 to g = .37); (b) ITS were less effective than human tutoring, but they outperformed all other instruction methods and learning activities, including traditional classroom instruction, reading printed text or computerized materials, computer-assisted instruction, laboratory or homework assignments, and no-treatment control; (c) ITS's effectiveness did not significantly differ by different ITS, subject domain, or the manner or degree of their involvement in instruction and learning; and (d) effectiveness in earlier studies appeared to be significantly greater than that in more recent studies. In addition, there is some evidence suggesting the importance of teachers and pedagogy in ITS-assisted learning.

Scholarly research productivity is not related to higher three-year licensure pass rates for physical therapy academic programs.

BACKGROUND: In the domain of academia, the scholarship of research may include, but not limited to, peer-reviewed publications, presentations, or grant submissions. Programmatic research productivity is one of many measures of academic program reputation and ranking. Another measure or tool for quantifying learning success among physical therapists education programs in the USA is 100 % three year pass rates of graduates on the standardized National Physical Therapy Examination (NPTE). In this study, we endeavored to determine if there was an association between research productivity through artifacts and 100 % three year pass rates on the NPTE. METHODS: This observational study involved using pre-approved database exploration representing all accredited programs in the USA who graduated physical therapists during 2009, 2010 and 2011. Descriptive variables captured included raw research productivity artifacts such as peer reviewed publications and books, number of professional presentations, number of scholarly submissions, total grant dollars, and numbers of grants submitted. Descriptive statistics and comparisons (using chi square and t-tests) among program characteristics and research artifacts were calculated. Univariate logistic regression analyses, with appropriate control variables were used to determine associations between research artifacts and 100 % pass rates. RESULTS: Number of scholarly artifacts submitted, faculty with grants, and grant proposals submitted were significantly higher in programs with 100 % three year pass rates. However, after controlling for program characteristics such as grade point average, diversity percentage of cohort, public/private institution, and number of faculty, there were no significant associations between scholarly artifacts and 100 % three year pass rates. CONCLUSIONS: Factors outside of research artifacts are likely better predictors for passing the NPTE.

VizMaps: A Bayesian Topic Modeling Based PubMed Search Interface

A common challenge that users of academic databases face is making sense of their query outputs for knowledge discovery. This is exacerbated by the size and growth of modern databases. PubMed, a central index of biomedical literature, contains over 25 million citations, and can output search results containing hundreds of thousands of citations. Under these conditions, efficient knowledge discovery requires a different data structure than a chronological list of articles. It requires a method of conveying what the important ideas are, where they are located, and how they are connected; a method of allowing users to see the underlying topical structure of their search. This paper presents VizMaps, a PubMed search interface that addresses some of these problems. Given search terms, our main backend pipeline extracts relevant words from the title and abstract, and clusters them into discovered topics using Bayesian topic models, in particular the Latent Dirichlet Allocation (LDA). It then outputs a visual, navigable map of the query results.