There are three choices: alpha-helix, beta-sheet, and random-coil. The idea is to take a linear sequence of amino acids and to predict, for each of these amino acids, what secondary structure it is a part of within the protein. This is a data set used by Ning Qian and Terry Sejnowski in their study using a neural net to predict the secondary structure of certain globular proteins. The data set was developed in collaboration with Ning Qian of Johns-Hopkins University. The data set was contributed to the benchmark collection by Terry Sejnowski, now at the Salk Institute and the University of California at San Deigo. Molecular Biology (Protein Secondary Structure) Data Setĭownload: Data Folder, Data Set DescriptionĪbstract: From CMU connectionist bench repository Classifies secondary structure of certain globular proteins It is the first instance of universal quantum algorithms beingĮmployed to tackle RNA folding problems, and our work provides a valuable modelįor utilizing universal quantum computers in solving RNA folding problems.Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Sequences, our quantum approach outperforms classical energy-based methods.Įxperimental results also indicate our method is robust in current noisyĭevices. Throughout our benchmark dataset, simulation results suggest that our quantumĪpproach is comparable in accuracy to classical methods. Performance with both numerical simulation and experimental realization. Solved through quantum approximation optimization algorithm. The state of a quadratic Hamiltonian, the whole folding process is described asĪ quadratic unconstrained binary optimization model. Mapped all possible secondary structure to Quantum algorithms will be presented, which are highly flexible and can beĪpplied to various physical devices. Universal quantum computing are not available. The folding of the secondary structure, highlighting that quantum computing isĪ promising solution to this problem. Recently, quantum annealer has rapidly predicted Sequences, while learning-based algorithms face challenges in obtaining

TraditionalĮnergy-based algorithms are short of precision, particularly for non-nested Sequences that to know how its secondary structure is formed.

Download a PDF of the paper titled Predicting RNA Secondary Structure on Universal Quantum Computer, by Ji Jiang and 7 other authors Download PDF Abstract: It is the first step for understanding how RNA structure folds from base

Secondary structure of protein download