Machine learning techniques for the prediction of indoor gamma-ray dose rates - Strengths, weaknesses and implications for epidemiology.

We investigate methods that improve the estimation of indoor gamma ray dose rates at locations where measurements had not been made. These new predictions use a greater range of modelling techniques and larger variety of explanatory variables than our previous examinations of this subject. Specifically, we now employ three types of machine learning models in addition to the geostatistical, nearest neighbour and other earlier models.

Gene model for the ortholog in .

Gene model for the ortholog of Myc ( ) in the May 2011 (Agencourt dana_caf1/DanaCAF1) Genome Assembly (GenBank Accession: GCA_000005115.1 ) of . This ortholog was characterized as part of a developing dataset to study the evolution of the Insulin/insulin-like growth factor signaling pathway (IIS) across the genus using the Genomics Education Partnership gene annotation protocol for Course-based Undergraduate Research Experiences.

An accessible digital imaging workflow for multiplexed quantitative analysis of adult eye phenotypes in .

The compound eye of has long been a model for studying genetics, development, neurodegeneration, and heterochromatin. Imaging and morphometry of adult and other insects is hampered by the low throughput, narrow focal plane, and small image sensors typical of stereomicroscope cameras. When data collection is distributed among many individuals or extended time periods, these limitations are compounded by inter-operator variability in lighting, sample positioning, focus, and post-acquisition processing.

Effects of stopping criterion on the growth of trees in regression random forests.

Random forests are a powerful machine learning tool that capture complex relationships between independent variables and an outcome of interest. Trees built in a random forest are dependent on several hyperparameters, one of the more critical being the node size. The original algorithm of Breiman, controls for node size by limiting the size of the parent node, so that a node cannot be split if it has less than a specified number of observations.

Smoothing Lexis diagrams using kernel functions: A contemporary approach.

Lexis diagrams are rectangular arrays of event rates indexed by age and period. Analysis of Lexis diagrams is a cornerstone of cancer surveillance research. Typically, population-based descriptive studies analyze multiple Lexis diagrams defined by sex, tumor characteristics, race/ethnicity, geographic region, etc.