ArticleBriefings in bioinformatics2022
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
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- SeqHIVE: a Python package to convert the biological sequences to informative vectors for sequence property predictions.BioData mining · 2026Article
- From nucleotides to numbers: a comprehensive review of RNA feature extraction methods for computational modelling.Briefings in bioinformatics · 2025Review
- DRP-PSM: Multi-Level Feature Integration Reveals Hierarchical Contributions to Pathogenic Synonymous Mutation Prediction.ACS omega · 2025Article
- Cytoplasmic localization of PUS7 facilitates a pseudouridine-dependent enhancement of cellular stress tolerance.bioRxiv : the preprint server for biology · 2025Article
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- BacTermFinder: a comprehensive and general bacterial terminator finder using a CNN ensemble.NAR genomics and bioinformatics · 2025Article
- Transcriptomics, Proteomics and Bioinformatics in Atrial Fibrillation: A Descriptive Review.Bioengineering (Basel, Switzerland) · 2025Review
- Prediction and validation of nanowire proteins inComputational and structural biotechnology journal · 2025Article
- LPItabformer: Enhancing generalization in predicting lncRNA-protein interactions via a tabular Transformer.Computational and structural biotechnology journal · 2025Article
- SaGP: identifying plant saline-alkali tolerance genes based on machine learning techniques.Frontiers in plant science · 2025Article
- GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs.Computational and structural biotechnology journal · 2024Article
- BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning.Computational and structural biotechnology journal · 2024Article
- RNA-ModX: a multilabel prediction and interpretation framework for RNA modifications.Briefings in bioinformatics · 2024Article
- When less is more: sketching with minimizers in genomics.Genome biology · 2024Review
- An ensemble deep learning model for predicting minimum inhibitory concentrations of antimicrobial peptides against pathogenic bacteria.iScience · 2024Article
- Genetically encoded transcriptional plasticity underlies stress adaptation in Mycobacterium tuberculosis.Nature communications · 2024Article
- Predicting condensate formation of protein and RNA under various environmental conditions.BMC bioinformatics · 2024Article
- DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction.BMC genomics · 2023Article
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4 authors.
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Abstract
One of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting numerical information from biological sequences have been reported in the literature. However, many of these techniques are not available in existing packages, such as mathematical descriptors. This paper presents a new package, MathFeature, which implements mathematical descriptors able to extract relevant numerical information from biological sequences, i.e. DNA, RNA and proteins (prediction of structural features along the primary sequence of amino acids). MathFeature makes available 20 numerical feature extraction descriptors based on approaches found in the literature, e.g. multiple numeric mappings, genomic signal processing, chaos game theory, entropy and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. To ensure that our descriptors are robust and to assess their relevance, experimental results are presented in nine case studies. According to these results, the features extracted by MathFeature showed high performance (0.6350-0.9897, accuracy), both applying only mathematical descriptors, but also hybridization with well-known descriptors in the literature. Finally, through MathFeature, we overcame several studies in eight benchmark datasets, exemplifying the robustness and viability of the proposed package. MathFeature has advanced in the area by bringing descriptors not available in other packages, as well as allowing non-experts to use feature extraction techniques.
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