Welcome to the Predict a Secondary Structure Web Server
2018年2月14日 · The Predict a Secondary Structure server combines four separate prediction and analysis algorithms: calculating a partition function, predicting a minimum free energy (MFE) structure, finding structures with maximum expected accuracy, and pseudoknot prediction.
List of RNA structure prediction software - Wikipedia
Secondary structure prediction method based on conditional log-linear models (CLLMs), a flexible class of probabilistic models which generalize upon SCFGs by using discriminative training and feature-rich scoring. Simple, cleanly written software to produce the full set of possible secondary structures for one sequence, given optional constraints.
Accurate RNA 3D structure prediction using a language model …
2024年11月21日 · Additionally, RhoFold+ predicts RNA secondary structures and interhelical angles, providing empirically verifiable features that broaden its applicability to RNA structure and function studies.
RNA secondary structure prediction using deep learning with ...
2021年2月11日 · Inspired by MXfold and the DNN-based RNA secondary structure prediction methods, in this paper, we propose an algorithm for predicting RNA secondary structures using deep learning.
Accurate prediction of RNA secondary structure including …
2024年3月9日 · Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of KnotFold include a learned potential function and a...
REDfold: accurate RNA secondary structure prediction using …
2023年3月28日 · RNA secondary structure prediction aims to predict an accurate base-pairing structure of a given RNA sequence. In this work, we proposed a fast and accurate structure prediction algorithm that predicts RNA secondary structure through the deep neural network.
UFold: fast and accurate RNA secondary structure prediction …
2021年11月18日 · Here, we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data and base-pairing rules. UFold proposes a novel image-like representation of RNA sequences, which can be efficiently processed by Fully Convolutional Networks (FCNs).
Wfold: A new method for predicting RNA secondary structure …
2024年11月1日 · This paper creates a deep neural network-based approach for RNA secondary structure prediction based just on a single RNA sequence. We present a unique DL model for RNA secondary structure prediction called Wfold, which includes strict limitations in its architecture design.
RNADiffFold: generative RNA secondary structure prediction …
2024年11月24日 · We introduce RNADiffFold, a novel discrete diffusion framework that treats RNA secondary structure prediction as a pixel-level segmentation task. This approach effectively captures the distribution of secondary conformations, unveiling valuable biological insights embedded in the primary sequence.
RNA secondary structure prediction with convolutional neural …
2022年2月2日 · We show how to use an artificial neural network design to predict the structure for a given RNA sequence with high accuracy only by learning from samples whose native structures have been experimentally characterized, independent of any energy model.