Computational Prediction of Pathogenic Network Modules in Fusarium verticillioides.

Fusarium verticillioides is a fungal pathogen that triggers stalk rots and ear rots in maize. In this study, we performed a comparative analysis of wild type and loss-of-virulence mutant F. verticillioides co-expression networks to identify subnetwork modules that are associated with its pathogenicity.

Computational identification of genetic subnetwork modules associated with maize defense response to Fusarium verticillioides.

Background: Maize, a crop of global significance, is vulnerable to a variety of biotic stresses resulting in economic losses. Fusarium verticillioides (teleomorph Gibberella moniliformis) is one of the key fungal pathogens of maize, causing ear rots and stalk rots. To better understand the genetic mechanisms involved in maize defense as well as F.

Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails.

Background: Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing.

Results: In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails.