Search Results for "level set methods"

In addition to computing co-expression clusters, Maccu fishes potential co-expressed genes for a given bait set. All results can be further processed via graph-level operations so that we can compare graphs based on different reference data.

...Finally, it permits effective interactions with other electronic structure codes [e.g., nuclear magnetic resonance (NMR), microwave] even if Gaussian 16 offers the widest set of methods for computational spectroscopy.

...Its architecture is both extensible and flexible using data structures that are intuitive to the biologist while transparently translating these into the underlying mathematical structures. . CBMPy implements popular analyses such as FBA, FVA, element/charge balancing, network analysis and model editing as well as advanced methods developed for the ecosystem modelling. CBMPy supports user interaction via: - interactive console or as a library for advanced use - GUI, visual representation of the model, analysis methods - a SOAP based webAPI exposes high level functionality via web services

AK Toolbox is a Matlab Toolbox for co-evolution analysis for protein Multiple Sequence Alignment (MSA) distributed under Simplified BSD License. The aim of AK toolbox is to provide a set of Matlab functions, which are independent of Matlab Bioinforamtics Toolbox, for coevolution anaylsis for MSA. At present, co-evolution methods available in this package are Statistical Coupling Analysis (SCA), Explicit Likelihood of Subset Covariance (ELSC), Mutual Information (MI), Observed Minus Expected Square method (OMES), McLanhlan Based Substitution Correlation (MCBASC), Direct-coupling analysis (DCA) and logR. ...

GENESIS (GEneral NEural SImulation System) is a software platform for the simulation of neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, large networks, and systems-level processes.

This project is an implementation of an algorithm by J. Keijsper and R. Pendavingh. The goal of the algorithm is to construct a phylogenetic level-1-network that is consistent with a given set of quartets.

Implematation of robust depth-based inference tools for microarray data (a scale curve, to measure the dispersion of a set of curves, a rank test to decide if two groups of curves come from the same population, and classification techniques).