ReviewFrontiers in bioengineering and biotechnology2020
Review on the Application of Machine Learning Algorithms in the Sequence Data Mining of DNA.
Review in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 154 citations in OpenAlex.
- Application of deep learning in cancer epigenetics through DNA methylation analysis.Briefings in bioinformatics · 2023Pooled it
- seqLens: Optimizing Language Models for Genomic Predictions.Molecular biology and evolution · 2026Article
- An explainable AI framework integrating machine and deep learning models for multi-species DNA functional group classification.Scientific reports · 2026Article
- A novel dynamic part-wise direct and complementary template matching approach for DNA sequence similarity identification.Molecular genetics and genomics : MGG · 2026Article
- Compliance patterns in adopting sustainability practices: A cluster analysis of oil palm producers in Colombia.Scientific reports · 2026Article
- SLC6A14 as a Key Diagnostic Biomarker for Ulcerative Colitis: An Integrative Bioinformatics and Machine Learning Approach.Biochemical genetics · 2026Article
- Development and validation of an interpretable machine learning model for non-invasive screening of precancerous gastric lesions using symptom and lifestyle data: a multicentre cohort study.EClinicalMedicine · 2026Article
- EDEN: multiscale expected density of nucleotide encoding for enhanced DNA sequence classification with hybrid deep learning.BMC bioinformatics · 2026Article
- A New Unsupervised Machine Learning Method for Metabarcoding Analysis Based on Selected Mock Community Data.Computational and structural biotechnology journal · 2026Article
- Mean shift based prototypical network for steel surface anomaly recognition.Scientific reports · 2025Article
- Genomic data representations for horizontal gene transfer detection.NAR genomics and bioinformatics · 2025Article
- Unveiling Berberine's Therapeutic Mechanisms Against Hepatocellular Carcinoma via Integrated Computational Biology and Machine Learning Approaches: AURKA and CDK1 as Principal Targets.International journal of molecular sciences · 2025Article
- Research Advances of Extrachromosomal Circular DNA in Breast Cancer.Cancer medicine · 2025Review
- Review
- Deep Genomics: Deep Learning-Based Analysis of Genome-Sequenced Data for Identification of Gene Alterations.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning.Foods (Basel, Switzerland) · 2024Article
- Striving for clarity in language about gene expression.Nucleic acids research · 2024Article
- Generative language models on nucleotide sequences of human genes.Scientific reports · 2024Article
- Finally Freed-Plants (Basel, Switzerland) · 2024Review
- PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning.International journal of molecular sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deoxyribonucleic acid (DNA) is a biological macromolecule. Its main function is information storage. At present, the advancement of sequencing technology had caused DNA sequence data to grow at an explosive rate, which has also pushed the study of DNA sequences in the wave of big data. Moreover, machine learning is a powerful technique for analyzing largescale data and learns spontaneously to gain knowledge. It has been widely used in DNA sequence data analysis and obtained a lot of research achievements. Firstly, the review introduces the development process of sequencing technology, expounds on the concept of DNA sequence data structure and sequence similarity. Then we analyze the basic process of data mining, summary several major machine learning algorithms, and put forward the challenges faced by machine learning algorithms in the mining of biological sequence data and possible solutions in the future. Then we review four typical applications of machine learning in DNA sequence data: DNA sequence alignment, DNA sequence classification, DNA sequence clustering, and DNA pattern mining. We analyze their corresponding biological application background and significance, and systematically summarized the development and potential problems in the field of DNA sequence data mining in recent years. Finally, we summarize the content of the review and look into the future of some research directions for the next step.
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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.