ArticleResearch (Washington, D.C.)2022
Biological Sequence Classification: A Review on Data and General Methods.
Article in Research (Washington, D.C.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- ACP-EPC: an interpretable deep learning framework for anticancer peptide prediction utilizing pre-trained protein language model and multi-view feature extracting strategy.Molecular diversity · 2026Article
- ProtT5-MSCRNet: a multi-scale convolutional and channel-recalibrated deep learning framework for anticancer peptide prediction.Frontiers in artificial intelligence · 2026Article
- An artificial intelligence-based approach for identifying the proteins regulating liquid-liquid phase separation.Briefings in bioinformatics · 2025Article
- Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction.BMC biology · 2025Article
- Review
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Exploring Biomass Precursor Synthesis as a Determinant in Microbial Adaptation to Unadapted Carbon Sources with AdaptUC.Research (Washington, D.C.) · 2025Article
- Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.IET systems biology · 2024Article
- DPNN-ac4C: a dual-path neural network with self-attention mechanism for identification of N4-acetylcytidine (ac4C) in mRNA.Bioinformatics (Oxford, England) · 2024Article
- Identifying nucleotide-binding leucine-rich repeat receptor and pathogen effector pairing using transfer-learning and bilinear attention network.Bioinformatics (Oxford, England) · 2024Article
- PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.Molecular diversity · 2024Article
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- Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur.BMC biology · 2024Article
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- MLR-predictor: a versatile and efficient computational framework for multi-label requirements classification.Frontiers in artificial intelligence · 2024Article
- Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis.Research (Washington, D.C.) · 2024Article
- Melatonin Induces Analgesic Effects through MTResearch (Washington, D.C.) · 2024Article
- Using the Random Forest for Identifying Key Physicochemical Properties of Amino Acids to Discriminate Anticancer and Non-Anticancer Peptides.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website (http://lab.malab.cn/~acy/BioseqData/home.html), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research.
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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.