This article reviews machine learning methods for bioinformatics. It presents modelling
methods, such as supervised classification, clustering and probabilistic graphical models for …

The organization, regulation and dynamical responses of biological systems are in many
cases too complex to allow intuitive predictions and require the support of mathematical …

Biochemical systems theory (BST) is the foundation for a set of analytical andmodeling tools
that facilitate the analysis of dynamic biological systems. This paper depicts major …

Designing and conducting experiments are routine practices for modern biologists. The real
challenge, especially in the post-genome era, usually comes not from acquiring data, but …

A recurrent neural network is proposed for solving the non-smooth convex optimization
problem with the convex inequality and linear equality constraints. Since the objective …

Rationale: Modern molecular biology is generating data of unprecedented quantity and
quality. Particularly exciting for biochemical pathway modeling and proteomics are …

This paper gives a comprehensive review of the application of metaheuristics to optimization
problems in systems biology, mainly focusing on the parameter estimation problem (also …

Motivation: To resolve the high-dimensionality of the genetic network inference problem in
the S-system model, a problem decomposition strategy has been proposed. While this …

Abstract Systems biology is a new discipline built upon the premise that an understanding of
how cells and organisms carry out their functions cannot be gained by looking at cellular …

Time series data can now be routinely collected for biochemical reaction pathways, and
recently, several methods have been proposed to infer reaction mechanisms for metabolic …