ReviewInternational journal of molecular sciences2016
Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition.
Review in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
18 citing papers in PubMed.
- A Machine Learning Approach to Identify C Type Lectin Domain (CTLD) Containing Proteins.The protein journal · 2024Article
- Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2023Review
- iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.Nucleic acids research · 2021Article
- A protein structural study based on the centrality analysis of protein sequence feature networks.PloS one · 2021Article
- Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning.Briefings in bioinformatics · 2020Article
- Sequence Alignment Using Machine Learning for Accurate Template-based Protein Structure Prediction.Bio-protocol · 2020Article
- Missing Value Estimation Methods Research for Arrhythmia Classification Using the Modified Kernel Difference-Weighted KNN Algorithms.BioMed research international · 2020Article
- A study on separation of the protein structural types in amino acid sequence feature spaces.PloS one · 2019Article
- Distinguishing mirtrons from canonical miRNAs with data exploration and machine learning methods.Scientific reports · 2018Article
- SDADB: a functional annotation database of protein structural domains.Database : the journal of biological databases and curation · 2018Article
- Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity.Frontiers in microbiology · 2018Article
- A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.BMC bioinformatics · 2017Article
- SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.BMC genomics · 2017Article
- Protein remote homology detection based on bidirectional long short-term memory.BMC bioinformatics · 2017Article
- Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification.Scientific reports · 2017Article
- Predicting influenza antigenicity from Hemagglutintin sequence data based on a joint random forest method.Scientific reports · 2017Article
- Article
- RNA-ligand molecular docking: advances and challenges.Wiley interdisciplinary reviews. Computational molecular scienceArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowledge on protein folding has a profound impact on understanding the heterogeneity and molecular function of proteins, further facilitating drug design. Predicting the 3D structure (fold) of a protein is a key problem in molecular biology. Determination of the fold of a protein mainly relies on molecular experimental methods. With the development of next-generation sequencing techniques, the discovery of new protein sequences has been rapidly increasing. With such a great number of proteins, the use of experimental techniques to determine protein folding is extremely difficult because these techniques are time consuming and expensive. Thus, developing computational prediction methods that can automatically, rapidly, and accurately classify unknown protein sequences into specific fold categories is urgently needed. Computational recognition of protein folds has been a recent research hotspot in bioinformatics and computational biology. Many computational efforts have been made, generating a variety of computational prediction methods. In this review, we conduct a comprehensive survey of recent computational methods, especially machine learning-based methods, for protein fold recognition. This review is anticipated to assist researchers in their pursuit to systematically understand the computational recognition of protein folds.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.