Search results
Results from the WOW.Com Content Network
List of notable protein secondary structure prediction programs. Name Method description Type Link Initial release RaptorX-SS8
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:
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 ...
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
This group of methods [11] [9] [12] [13] [14] makes use of known protein complex structures to predict and structurally model interactions between query protein sequences. The prediction process generally starts by employing a sequence based method (e.g. Interolog) to search for protein complex structures that are homologous to the query ...
[39] [42] Disadvantages of some function prediction algorithms have included a lack of accessibility, and the time required for analysis. Faster, more accurate algorithms such as GeneMANIA (multiple association network integration algorithm) have however been developed in recent years [ 40 ] and are publicly available on the web, indicating the ...
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 ...
For proteins, this means predicting the formation of protein structures such as alpha helices and beta strands, while for nucleic acids it means predicting the formation of nucleic acid structures like helixes and stem-loop structures through base pairing and base stacking interactions. Secondary structure prediction can refer to: