The traditional approach to computational biophysics studies of molecular systems is brute force molecular dynamics simulations under the conditions of interest. The disadvantages of this approach are that the time and length scales that are accessible to computer simulations often do not reach biologically relevant scales. An alternative approach, which we call intuitive modeling, is hypothesi...

It is becoming increasingly important to learn from a partially-observed random matrix and predict its missing elements. We assume that the entire matrix is a single sample drawn from a matrix-variate t distribution and suggest a matrixvariate tmodel (MVTM) to predict those missing elements. We show that MVTM generalizes a range of known probabilistic models, and automatically performs model se...

Most machine learning, data mining and statistical methods rely on the assumption that the analyzed data points are independent and identically distributed (i.i.d.). More specifically, the individual examples included in the training data are assumed to be drawn independently from each other from the same probability distribution. However, cases where this assumption is violated can be easily f...

Predictive models of gene regulation Anshul Bharat Kundaje The regulation of gene expression plays a central role in the development and function of a living cell. A complex network of interacting regulatory proteins bind specific sequence elements in the genome to control the amount and timing of gene expression. The abundance of genome-scale datasets from different organisms provides an oppor...

Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...