ArticleBioData mining2015
LAF: Logic Alignment Free and its application to bacterial genomes classification.
Article in BioData mining, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Evaluation of Arabian Vascular Plant Barcodes (rbcL and matK): Precision of Unsupervised and Supervised Learning Methods towards Accurate Identification.Plants (Basel, Switzerland) · 2021Article
- ML-DSP: Machine Learning with Digital Signal Processing for ultrafast, accurate, and scalable genome classification at all taxonomic levels.BMC genomics · 2019Article
- An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes.PloS one · 2018Article
- Decision Tree Algorithm-Generated Single-Nucleotide Polymorphism Barcodes ofEvolutionary bioinformatics online · 2018Article
- Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models.PeerJ · 2016Article
- Article
- MISSEL: a method to identify a large number of small species-specific genomic subsequences and its application to viruses classification.BioData mining · 2016Article
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3 authors.
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Abstract
Alignment-free algorithms can be used to estimate the similarity of biological sequences and hence are often applied to the phylogenetic reconstruction of genomes. Most of these algorithms rely on comparing the frequency of all the distinct substrings of fixed length (k-mers) that occur in the analyzed sequences. In this paper, we present Logic Alignment Free (LAF), a method that combines alignment-free techniques and rule-based classification algorithms in order to assign biological samples to their taxa. This method searches for a minimal subset of k-mers whose relative frequencies are used to build classification models as disjunctive-normal-form logic formulas (if-then rules). We apply LAF successfully to the classification of bacterial genomes to their corresponding taxonomy. In particular, we succeed in obtaining reliable classification at different taxonomic levels by extracting a handful of rules, each one based on the frequency of just few k-mers. State of the art methods to adjust the frequency of k-mers to the character distribution of the underlying genomes have negligible impact on classification performance, suggesting that the signal of each class is strong and that LAF is effective in identifying it.
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