Search Results for "complex"

...You can also download SPiP in zip format: https://sourceforge.net/projects/splicing-prediction-pipeline/files/SPiP_alone_v2_1.zip For Linux (GitHub): https://github.com/LBGC-CFB/SPiP If you have questions, please contact me to: r.leman@baclesse.unicancer.fr or raphael.leman@orange.fr v2.1 corrected (07/2021) : correction of score calculation v2.1 (06/2021): Most strigent on "Alter by complex event" v2.0 (04/2021): New SPiP with random forest modelization old change detailled in https://sourceforge.net/projects/splicing-prediction-pipeline/files/changeLogBeforev2.0.txt

VLE is a multi-modeling and simulation environment to study complex dynamic systems. VLE is based on the discrete event specification DEVS. and it implements the DSDE formalism (A merge of Dynamic Structure DEVS, DSDEVS, with Parallel DEVS, PDEVS). VLE provides a complete set of C++ libraries, called VFL (VLE Foundation Libraries), to develop DEVS models, to gets results of simulations, to launch simulation on cluster.

Living cells are complex, dynamic, self-regulatory, interactive systems, showing differential states across time and space. Complexity of cellular systems is highlighted with the multi-layered regulatory mechanisms involving the interactions between bio-molecules (such as DNA, RNA, mi-RNA and proteins). These interactions are analyzed in the form of static networks.

In metabolomics the goal is to identify and measure the concentrations of different metabolites (small molecules) in a cell or a biological system. The metabolites form an important layer in the complex metabolic network, and the interactions between different metabolites are often of interest. It is crucial to perform proper normalization of metabolomics data and here we share the code for a normalization approach based on a mixed model, with simultaneous estimation of a correlation matrix. The methodology is implemented in R.

...The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems).

msBayes allows complex and flexible phylogeographic inference. More specifically, you can test the simultaneous divergence (TSD) of multiple population (species) pairs. It uses approximate Bayesian computation (ABC) under a hierarchical model.

ClinStudyWeb is designed to provide a flexible infrastructure for managing patient and assay data from clinical studies. It uses a plugin system for study-specific web forms and arbitrarily complex test classifiers, and supports XML import/export.