At the intersection between artificial intelligence and statistics, supervised learning allows
algorithms to automatically build predictive models from just observations of a system …

Plant responses to environmental and intrinsic signals are tightly controlled by multiple
transcription factors (TFs). These TFs and their regulatory connections form gene regulatory …

One of the pressing open problems of computational systems biology is the elucidation of
the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in …

Cooperativity between transcription factors is critical to gene regulation. Current
computational methods do not take adequate account of this salient aspect. To address this …

Knowing transcription factors (TFs) involved in the yeast cell cycle is helpful for
understanding the regulation of yeast cell cycle genes. We therefore developed two …

Background MicroRNAs (miRNAs) are post-transcriptional regulators of mRNA expression
and are involved in numerous cellular processes. Consequently, miRNAs are an important …

Background The importance of in silico predictions for understanding cellular processes is
now widely accepted, and a variety of algorithms useful for studying different biological …

In this chapter, we introduce the reader to a popular family of machine learning algorithms,
called decision trees. We then review several approaches based on decision trees that have …

Objective: The present investigation comprises, in vitro antidiabetic activity such as α-
amylase and α-glucosidase inhibitory activities and in vivo antidiabetic activity of methanolic …

The recent availability of whole-genome scale data sets that investigate complementary and
diverse aspects of transcriptional regulation has spawned an increased need for new and …