Impact of Multi-Factor Features on Protein Secondary Structure Prediction [PDF]
BiomoleculesProtein secondary structure prediction (PSSP) plays a crucial role in resolving protein functions and properties. Significant progress has been made in this field in recent years, and the use of a variety of protein-related features, including amino acid
Benzhi Dong +5 more
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RNA secondary structure prediction with convolutional neural networks [PDF]
BMC Bioinformatics, 2022 Background Predicting the secondary, i.e. base-pairing structure of a folded RNA strand is an important problem in synthetic and computational biology.
Mehdi Saman Booy +2 more
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NTFold: Structure-Sensing Nucleotide Attention Learning for RNA Secondary Structure Prediction [PDF]
SensorsDetermining RNA secondary structures is a fundamental challenge in computational biology and molecular sensing. Experimental techniques such as X-ray crystallography, nuclear magnetic resonance, and cryo-electron microscopy can reveal RNA structures with
Kangjun Jin +4 more
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Deep learning for protein secondary structure prediction: Pre and post-AlphaFold [PDF]
Computational and Structural Biotechnology Journal, 2022 This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure
Dewi Pramudi Ismi +2 more
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Review of machine learning methods for RNA secondary structure prediction. [PDF]
PLoS Computational Biology, 2021 Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure.
Qi Zhao +5 more
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Secondary structure prediction for RNA sequences including N6-methyladenosine [PDF]
Nature Communications, 2022 RNA folding free energy nearest neighbor parameters were determined for sequences with the nucleotide m6A. The RNAstructure software package can accommodate modified nucleotides, enabling secondary structure prediction of sequences with m6A.
Elzbieta Kierzek +6 more
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RNA secondary structure prediction by conducting multi-class classifications [PDF]
Computational and Structural Biotechnology JournalGenerating valid predictions of RNA secondary structures is challenging. Several deep learning methods have been developed for predicting RNA secondary structures.
Jiyuan Yang +4 more
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RNA independent fragment partition method based on deep learning for RNA secondary structure prediction [PDF]
Scientific Reports, 2023 The non-coding RNA secondary structure largely determines its function. Hence, accuracy in structure acquisition is of great importance. Currently, this acquisition primarily relies on various computational methods.
Qi Zhao +7 more
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Efficient Generation of RNA Secondary Structure Prediction Algorithm Under PAR Framework [PDF]
Frontiers in Plant Science, 2022 Prediction of RNA secondary structure is an important part of bioinformatics genomics research. Mastering RNA secondary structure can help us to better analyze protein synthesis, cell differentiation, metabolism, and genetic processes and thus reveal the
Haihe Shi, Xiaoqian Jing
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Discovering the Ultimate Limits of Protein Secondary Structure Prediction [PDF]
Biomolecules, 2021 Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy.
Chia-Tzu Ho +4 more
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