ArticleScientific reports2018
Protein Secondary Structure Prediction Based on Data Partition and Semi-Random Subspace Method.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
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- Harnessing computational immunology to design targeted subunit vaccines for infectious bursal disease in poultry.Frontiers in bioinformatics · 2025Article
- MHTAPred-SS: A Highly Targeted Autoencoder-Driven Deep Multi-Task Learning Framework for Accurate Protein Secondary Structure Prediction.International journal of molecular sciences · 2024Article
- Bioinformatics designing of an mRNA vaccine for Mokola virus (MOKV) using immunoinformatics as a secure strategy for successful vaccine development.BMC immunology · 2024Article
- In Silico Analysis: Genome-Wide Identification, Characterization and Evolutionary Adaptations of Bone Morphogenetic Protein (BMP) Gene Family in Homo sapiens.Molecular biotechnology · 2024Article
- HIV-1 Vif protein sequence variations in South African people living with HIV and their influence on Vif-APOBEC3G interaction.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2024Article
- Molecular genetic variability ofFrontiers in microbiology · 2024Article
- GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier.BMC genomics · 2023Article
- Designing multi-epitope vaccine against important colorectal cancer (CRC) associated pathogens based on immunoinformatics approach.BMC bioinformatics · 2023Article
- Structural mechanism of BRD4-NUT and p300 bipartite interaction in propagating aberrant gene transcription in chromatin in NUT carcinoma.Nature communications · 2023Article
- Ensemble deep learning models for protein secondary structure prediction using bidirectional temporal convolution and bidirectional long short-term memory.Frontiers in bioengineering and biotechnology · 2023Article
- Bioinformatics Designing and Molecular Modelling of a Universal mRNA Vaccine for SARS-CoV-2 Infection.Vaccines · 2022Article
- DLBLS_SS: protein secondary structure prediction using deep learning and broad learning system.RSC advances · 2022Article
- A comprehensive review on genomics, systems biology and structural biology approaches for combating antimicrobial resistance in ESKAPE pathogens: computational tools and recent advancements.World journal of microbiology & biotechnology · 2022Review
- Wide-Line NMR Melting Diagrams, Their Thermodynamic Interpretation, and Secondary Structure Predictions for A30P and E46K α-Synuclein.ACS omega · 2022Article
- Contiguously hydrophobic sequences are functionally significant throughout the human exome.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Molecular Modeling of Subtype-Specific Tat Protein Signatures to Predict Tat-TAR Interactions That May Be Involved in HIV-Associated Neurocognitive Disorders.Frontiers in microbiology · 2022Article
- Secondary Structures of Proteins: A Comparison of Models and Experimental Results.Journal of proteome research · 2021Article
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3 authors.
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Abstract
Protein secondary structure prediction is one of the most important and challenging problems in bioinformatics. Machine learning techniques have been applied to solve the problem and have gained substantial success in this research area. However there is still room for improvement toward the theoretical limit. In this paper, we present a novel method for protein secondary structure prediction based on a data partition and semi-random subspace method (PSRSM). Data partitioning is an important strategy for our method. First, the protein training dataset was partitioned into several subsets based on the length of the protein sequence. Then we trained base classifiers on the subspace data generated by the semi-random subspace method, and combined base classifiers by majority vote rule into ensemble classifiers on each subset. Multiple classifiers were trained on different subsets. These different classifiers were used to predict the secondary structures of different proteins according to the protein sequence length. Experiments are performed on 25PDB, CB513, CASP10, CASP11, CASP12, and T100 datasets, and the good performance of 86.38%, 84.53%, 85.51%, 85.89%, 85.55%, and 85.09% is achieved respectively. Experimental results showed that our method outperforms other state-of-the-art methods.
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