Variable selection for BART: An application to gene regulation

We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels. This can be viewed as a variable selection problem, potentially with high dimensionality. Variable selection is especially challenging in high-dimensional settings, where it is difficult to detect subtle individual effects and interactions between predictors. Bayesian Additive Regression Trees [BART, Ann. Appl. Stat. 4 (2010) 266-298] provides a novel nonparametric alternative to parametric regression approaches, such as the lasso or stepwise regression, especially when the number of relevant predictors is sparse relative to the total number of available predictors and the fundamental relationships are nonlinear. We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real. Going further, we adapt the BART procedure to incorporate informed prior information about variable importance. We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings. To demonstrate the potential of our approach in a biological context, we apply it to the task of inferring the gene regulatory network in yeast (Saccharomyces cerevisiae). We find that our BART-based procedure is best able to recover the subset of covariates with the largest signal compared to other variable selection methods. The methods developed in this work are readily available in the R package bartMachine.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS755 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Wheat Marketing Strategies

Wheat prices were very strong during the end of November. Producers may have chosen to wrap up sales of old crop wheat on that strength. For those with wheat in the bin, modest price strength should be regarded as an opportunity to complete sales, especially if prices are as high as during late November. Scale-up selling of anticipated 1994 production on the Minneapolis Grain Exchange (MGE) at $.350 September futures or better should allow many producers to realize at least$3.00 on the farm, the anticipated 1994-95 seasonal average farm price. Such an opportunity should be captured whether in December, February, or May. For much higher prices to materialize, traders may have to see the development of unfavorable growing conditions

Sunflower Marketing Strategies

Price history suggests that any remaining old crop sunseed should be sold now. The sunflower (oil type) price in early April was a high as any price since 1986 (Fig 1). The average price of sunseed at Enderlin, N.D., from harvest in 1985 to March 1994 was 9.53 ¢/lb. Old crop price bids of 14 to 15 ¢/lb offered during early April not only exceed the long-term average price but also exceeded the 5-year average of 10.37 ¢/lb. A premium also was paid for high oil content. The new crop price level suggest that a set-up marketing plan for new crop should be considered. The new crop Enderlin sunseed bid exceeded the 5-year average most of the winter and was near 11 ¢/lb in early April. Farm level or local prices should have been around 10.5 ¢/lb. Forward pricing 30 to 40% of expected 1994 production by June 1, and more later if a weather rally occurs, may be a suitable plan for many producers. The national loan rate provides a floor price of 8.72 ¢/lb in case of lower prices later in the year. Remember, sunflowers produced in 0/92 acres as well as other acres on the same ASCS farm unit do not qualify for loan if the deficiency payment on the base crop is accepted

Late Planting Implications for Marketing Spring Wheat

A window of opportunity for selling the balance of old crop spring wheat as well as a portion of anticipated production may be availing itself to producers. Prices for old crop and new crop spring wheat have increased sharply in response to planting delays

Giving students the choice of authentic assessments in the Chemistry laboratory

Incorporating authentic assessments into a laboratory program can improve students’ engagement and satisfaction whilst developing highly valuable employment skills (Schultz et al., 2022; Sokhanvar et al., 2021). It can promote a deeper understanding by having students translate their data and findings to a real-world audience of stakeholders – occasionally not from the scientific community (Jopp, 2020). Another assessment strategy is to empower students with a choice of assessments to complete. A choice of assessments can increase student motivation, confidence and engagement whilst reducing the anxiety around assessments (Patall et al., 2010; Garside et al., 2009). This promotes self-regulated learning, self-efficacy and can lead to improved academic performance (Jopp & Cohen, 2020). At The University of Sydney, students complete four experiments in their laboratory program during their first semester of first year chemistry. Each experiment has a different style of authentic assessment associated with it. Students are provided with the opportunity to select one of these four experiments (and its associated authentic assessment) to submit as their major laboratory assessment for the semester. At the end of semester, students (n = ~750) were questioned about why they had selected the assessment they submitted. In this presentation, we analyse how popular the different authentic assessments were amongst students and look at some of the motives behind why these assessments were selected. REFERENCES Garside, J., Nhemachena, J. Williams, J., & Topping, A. (2009). Repositioning assessment: Giving students the ‘choice’ of assessment methods. Nurse Education in Practice, 9(2), 141-148. Jopp, R. (2020). A case study of a technology enhanced learning initiative that supports authentic assessment. Teaching in Higher Education, 25(8), 942-958. https://doi.org/10.1080/13562517.2019.1613637 Jopp, R. & Cohen, J. (2020). Choose your own assessment – assessment choice for students in online higher education. Teaching in Higher Education 27(6), 738-755. https://doi.org/10.1080/13562517.2020.1742680 Patall, E. A., Cooper, H., & Wynn, S.R. (2010). The effectiveness and relative importance of choice in the classroom. Journal of Educational Psychology, 102(4), 896. Schultz, M., Young, K., Gunning, T., & Harvey, M. (2022). Defining and measuring authentic assessment: a case study in the context of tertiary science. Assessment and Evaluation in Higher Education, 47(1), 77-94. Sokhanvar, Z. Salehi, K., & Sokhanvar, F. (2021) Advantages of authentic assessment for the improving the learning experience and employability skills of higher education students: A systematic literature review. Studies in Educational Evaluation, 70, 101030

Speed Cut-Off Point for Antiforce Waves

A one-dimensional, three-component, fluid model has been employed to investigate the existence of a speed cut-off point for antiforce breakdown waves. The term antiforce wave is used to identify breakdown waves for which the electric field force on electrons is in the opposite direction of wave propagation. The electron fluid-dynamical equations for antiforce waves are different from those of proforce waves. This presentation will address the difference in the set of equations for proforce and antiforce waves and the method of integration of the set of equations through the dynamical transition region for antiforce waves. Also, for antiforce waves, the existence and approximate value of a speed cut-off point will be discussed

Clustering Areal Units at Multiple Levels of Resolution to Model Crime in Philadelphia

Estimation of the spatial heterogeneity in crime incidence across an entire city is an important step towards reducing crime and increasing our understanding of the physical and social functioning of urban environments. This is a difficult modeling endeavor since crime incidence can vary smoothly across space and time but there also exist physical and social barriers that result in discontinuities in crime rates between different regions within a city. A further difficulty is that there are different levels of resolution that can be used for defining regions of a city in order to analyze crime. To address these challenges, we develop a Bayesian non-parametric approach for the clustering of urban areal units at different levels of resolution simultaneously. Our approach is evaluated with an extensive synthetic data study and then applied to the estimation of crime incidence at various levels of resolution in the city of Philadelphia