The promise of big data is enormous and nowhere is it more critical than in its potential to
contain important, undiscovered interdependence among thousands of variables. Networks …

Transcriptional regulation plays vital roles in many fundamental biological processes.
Reverse engineering of genome-wide regulatory networks from high-throughput …

Mathematical models of natural systems are abstractions of much more complicated
processes. Develo** informative and realistic models of such systems typically involves …

Assuming stationarity is unrealistic in many time series applications. A more realistic
alternative is to assume piecewise stationarity, where the model can change at potentially …

Getting pregnant is usually easy and fun, but the gestation and delivery may be another
story; messy and painful perhaps, but instructive nevertheless. So it is with this book, which …

The gene regulation network (GRN) is a high-dimensional complex system, which can be
represented by various mathematical or statistical models. The ordinary differential equation …

We consider the task of learning a dynamical system from high-dimensional time-course
data. For instance, we might wish to estimate a gene regulatory network from gene …

Kriging (or Gaussian process regression) becomes a popular machine learning method for
its flexibility and closed-form prediction expressions. However, one of the key challenges in …

Despite increasing emphasis on the genetic study of quantitative traits, we are still far from
being able to chart a clear picture of their genetic architecture, given an inherent complexity …

Differential equations have proven to be a powerful mathematical tool in science and
engineering, leading to better understanding, prediction, and control of dynamic processes …