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A modular open source platform to provide a powerful, yet flexible general working environment for structural bioinformatics. OpenStructure consists primarily of a set of libraries written in C++ with a cleanly designed application programmer interface. All functionality can be accessed directly in C++ or in a Python layer, meeting both the requirements for high efficiency and ease of use. Powerful selection queries and the notion of entity views to represent these selections greatly facilitate the development and implementation of algorithms on structural data. The modular integration of computational core methods with powerful visualization tools makes OpenStructure an ideal working and development environment.
A mature C programming library for manipulating protein structure. The library also provides a small number of functions for handling protein sequence as well as general purpose programming and mathematics. BiopLib transparently handles PDBML (XML) format and standard PDB files. The core of the BiopLib library is a reliable PDB parser that handles alternate occupancies and deals with compressed PDB files and PDBML files automatically. The library is designed to be as flexible as possible, allowing users to handle PDB data as a simple list of atoms, or in a structured form using chains, residues and atoms.
BALL / Biochemical Algorithms Library
A comprehensive rapid application development framework for structural bioinformatics. BALL provides an extensive C++ class library of data structures and algorithms for molecular modeling and structural bioinformatics. Using BALL as a programming toolbox does not only allow to greatly reduce application development times but also helps in ensuring stability and correctness by avoiding the error-prone reimplementation of complex algorithms and replacing them with calls into the library that has been well-tested by a large number of developers.
Allows to calculate the center of mass of a protein. CALCOM can estimate the cartesian coordinates of the center of mass of a single chain, as well as complexed chains and ligands. It can be used to evaluate their distance in protein-protein and protein-small ligand complexes, and to measure the distance of selected amino acids and atoms to the center of mass. The tool was used to evaluate the distance between two subunits of the miraculin dimer during the molecular dynamics simulations performed at different pH conditions.
An implementation of support vector machine (SVM) for the problem of pattern recognition, for the problem of regression, and for the problem of learning a ranking function. SVMlight provides methods for assessing the generalization performance efficiently. It includes two efficient estimation methods for both no error rate and precision/recall. The algorithm proceeds by solving a sequence of optimization problems lower-bounding the solution using a form of local search. SVMlight has been used on a large range of problems, including text classification, image recognition tasks, bioinformatics and medical applications.
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