MOTIVATION: Many ordinary differential equation (ODE) models have been introduced to replace linear regression models for inferring gene regulatory relationships from time-course gene expression data. But, since the observed data are usually not direct measurements of the gene products or there is an unknown time lag in gene regulation, it is problematic to directly apply traditional ODE models or linear regression models. RESULTS: We introduce a lagged ODE model to infer lagged gene regulatory relationships from time-course measurements, which are modeled as linear transformation of the gene products. A time-course microarray dataset from a yeast cell-cycle study is used for simulation assessment of the methods and real data analysis. The results show that our method, by considering both time lag and measurement scaling, performs much better than other linear and ODE models. It indicates the necessity of explicitly modeling the time lag and measurement scaling in ODE gene regulatory models. AVAILABILITY AND IMPLEMENTATION: R code is available at https://www.sta.cuhk.edu.hk/xfan/share/lagODE.zip.

Topic modeling has been an important field in natural language processing (NLP) and recently witnessed great methodological advances. Yet, the development of topic modeling is still, if not increasingly, challenged by two critical issues. First, despite intense efforts toward nonparametric/post-training methods, the search for the optimal number of topics K remains a fundamental question in topic modeling and warrants input from domain experts. Second, with the development of more sophisticated models, topic modeling is now ironically been treated as a black box and it becomes increasingly difficult to tell how research findings are informed by data, model specifications, or inference algorithms. Based on about 120,000 newspaper articles retrieved from three major Canadian newspapers (Globe and Mail, Toronto Star, and National Post) since 1977, we employ five methods with different model specifications and inference algorithms (Latent Semantic Analysis, Latent Dirichlet Allocation, Principal Component Analysis, Factor Analysis, Nonnegative Matrix Factorization) to identify discussion topics. The optimal topics are then assessed using three measures: coherence statistics, held-out likelihood (loss), and graph-based dimensionality selection. Mixed findings from this research complement advances in topic modeling and provide insights into the choice of optimal topics in social science research.