Benchmarking regulatory network reconstruction with GRENDEL

Benchmarking regulatory network reconstruction with GRENDEL

MOTIVATION Over the past decade, the prospect of inferring networks of gene regulation from high-throughput experimental data has received a great deal of attention. In contrast to the massive effort that has gone into automated deconvolution of biological networks, relatively little effort has been invested in benchmarking the proposed algorithms. The rate at which new network inference method...

GeneSPIDER - gene regulatory network inference benchmarking with controlled network and data properties.

A key question in network inference, that has not been properly answered, is what accuracy can be expected for a given biological dataset and inference method. We present GeneSPIDER - a Matlab package for tuning, running, and evaluating inference algorithms that allows independent control of network and data properties to enable data-driven benchmarking. GeneSPIDER is uniquely suited to address...

Regulatory Network Reconstruction Using Stochastic Logical Networks

This paper presents a method for regulatory network reconstruction from experimental data. We propose a mathematical model for regulatory interactions, based on the work of Thomas et al. [25] extended with a stochastic element and provide an algorithm for reconstruction of such models from gene expression time series. We examine mathematical properties of the model and the reconstruction algori...

Functional Transcription Regulatory Network Reconstruction and Characterization

Benchmarking network processors

In this paper the need for standardized benchmarks for network processors are discussed. Benchmarks are important for developers of high performance network related hardware who need to decide upon which Network Processor to use. The difference between application level benchmarks and micro level benchmarks are discussed. High quality micro level benchmarks are proposed as a way of easily assem...