ArticleBMC bioinformatics2012
A novel hierarchical clustering algorithm for gene sequences.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Clustering methods for categorical time series and sequences : a scoping review.BMC medical research methodology · 2026Article
- mtDNA "nomenclutter" and its consequences on the interpretation of genetic data.BMC ecology and evolution · 2024Article
- Comparative Transcriptome Analysis Unveils Regulatory Factors Influencing Fatty Liver Development in Lion-Head Geese under High-Intake Feeding Compared to Normal Feeding.Veterinary sciences · 2024Article
- Accurately clustering biological sequences in linear time by relatedness sorting.Nature communications · 2024Article
- Analysis of Emerging Variants of Turkey Reovirus using Machine Learning.Briefings in bioinformatics · 2024Article
- AlignScape, displaying sequence similarity using self-organizing maps.Frontiers in bioinformatics · 2024Article
- Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches.Gastroenterology and hepatology from bed to bench · 2024Review
- PWSC: a novel clustering method based on polynomial weight-adjusted sparse clustering for sparse biomedical data and its application in cancer subtyping.BMC bioinformatics · 2023Article
- HycDemux: a hybrid unsupervised approach for accurate barcoded sample demultiplexing in nanopore sequencing.Genome biology · 2023Article
- SCRAPT: an iterative algorithm for clustering large 16S rRNA gene data sets.Nucleic acids research · 2023Article
- Benchmarking machine learning robustness in Covid-19 genome sequence classification.Scientific reports · 2023Article
- Review
- Clustering biological sequences with dynamic sequence similarity threshold.BMC bioinformatics · 2022Article
- The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers.Entropy (Basel, Switzerland) · 2021Article
- Androgenic Sensitivities and Ovarian Gene Expression Profiles Prior to Treatment in Japanese Eel (Anguilla japonica).Marine biotechnology (New York, N.Y.) · 2021Article
- Clustering and classification of virus sequence through music communication protocol and wavelet transform.Genomics · 2021Article
- DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina.International journal of molecular sciences · 2020Article
- A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.PLoS computational biology · 2020Article
- Review on the Application of Machine Learning Algorithms in the Sequence Data Mining of DNA.Frontiers in bioengineering and biotechnology · 2020Review
- A survey and evaluations of histogram-based statistics in alignment-free sequence comparison.Briefings in bioinformatics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClustering DNA sequences into functional groups is an important problem in bioinformatics. We propose a new alignment-free algorithm, mBKM, based on a new distance measure, DMk, for clustering gene sequences. This method transforms DNA sequences into the feature vectors which contain the occurrence, location and order relation of k-tuples in DNA sequence. Afterwards, a hierarchical procedure is applied to clustering DNA sequences based on the feature vectors.
resultsThe proposed distance measure and clustering method are evaluated by clustering functionally related genes and by phylogenetic analysis. This method is also compared with BlastClust, CD-HIT-EST and some others. The experimental results show our method is effective in classifying DNA sequences with similar biological characteristics and in discovering the underlying relationship among the sequences.
conclusionsWe introduced a novel clustering algorithm which is based on a new sequence similarity measure. It is effective in classifying DNA sequences with similar biological characteristics and in discovering the relationship among the sequences.
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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.