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A dynamic programming algorithm for the prediction of a restricted class (H-type) of RNA pseudoknots. Yes: sourcecode, webserver [20] RNA123: Secondary structure prediction via thermodynamic-based folding algorithms and novel structure-based sequence alignment specific for RNA. Yes: webserver: RNAfold: MFE RNA structure prediction algorithm.
G-quadruplex structures can be computationally predicted from DNA or RNA sequence motifs, [11] [12] but their actual structures can be quite varied within and between the motifs, which can number over 100,000 per genome. Their activities in basic genetic processes are an active area of research in telomere, gene regulation, and functional ...
One of the easiest ways to understand algorithms for general structured prediction is the structured perceptron by Collins. [3] This algorithm combines the perceptron algorithm for learning linear classifiers with an inference algorithm (classically the Viterbi algorithm when used on sequence data) and can be described abstractly as follows:
A unified interface for: Tertiary structure prediction/3D modelling, 3D model quality assessment, Intrinsic disorder prediction, Domain prediction, Prediction of protein-ligand binding residues Automated webserver and some downloadable programs RaptorX: remote homology detection, protein 3D modeling, binding site prediction
Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas. [2] [3] [4] It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution, traffic ...
The protein structure prediction remains an extremely difficult and unresolved undertaking. The two main problems are the calculation of protein free energy and finding the global minimum of this energy. A protein structure prediction method must explore the space of possible protein structures which is astronomically large.
Go is designed for the "speed of working in a dynamic language like Python" [239] and shares the same syntax for slicing arrays. Groovy was motivated by the desire to bring the Python design philosophy to Java. [240] Julia was designed to be "as usable for general programming as Python". [27]
To learn the graph structure as a multivariate Gaussian graphical model, we can use either L-1 regularization, or neighborhood selection algorithms. These algorithms simultaneously learn a graph structure and the edge strength of the connected nodes. An edge strength corresponds to the potential function defined on the corresponding two-node ...