Andrea Pinna

TL;DR: A comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data defines the performance, data requirements and inherent biases of different inference approaches, and provides guidelines for algorithm application and development.

TL;DR: Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for next-generation sequencing microbiology data analysis provides new opportunities for data-intensive computational analyses in microbiology and metagenomics.

TL;DR: An inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges, is proposed, proving to be effective in inferring medium-size gene regulatory networks.

TL;DR: SysGenSIM allows the user to select a variety of network topologies, genetic and kinetic parameters to simulate SG data ( genotyping, gene expression and phenotyping) with large gene networks with thousands of nodes.

TL;DR: In this article, an aerogel with durable hydrophobicity is produced by incorporating reduced graphene oxide (rGO) in highly porous silica matrices by a sol-gel protocol, achieving high loadings by exploiting highly dispersible hydrophilic graphene oxide as rGO precursor.