ArticleBioData mining2011
Interpol: An R package for preprocessing of protein sequences.
Article in BioData mining, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Chaos game representation and its applications in bioinformatics.Computational and structural biotechnology journal · 2021Review
- Encodings and models for antimicrobial peptide classification for multi-resistant pathogens.BioData mining · 2019Review
- A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors.Molecules (Basel, Switzerland) · 2018Article
- COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator.BMC bioinformatics · 2016Article
- Learning the Relationship between the Primary Structure of HIV Envelope Glycoproteins and Neutralization Activity of Particular Antibodies by Using Artificial Neural Networks.International journal of molecular sciences · 2016Article
- SHIVA - a web application for drug resistance and tropism testing in HIV.BMC bioinformatics · 2016Article
- Genotypic Prediction of Co-receptor Tropism of HIV-1 Subtypes A and C.Scientific reports · 2016Article
- Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification.BioData mining · 2016Article
- A simple structure-based model for the prediction of HIV-1 co-receptor tropism.BioData mining · 2014Article
- 3'-Phosphoadenosine 5'-phosphosulfate (PAPS) synthases, naturally fragile enzymes specifically stabilized by nucleotide binding.The Journal of biological chemistry · 2012Article
- Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers.BioData mining · 2011Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMost machine learning techniques currently applied in the literature need a fixed dimensionality of input data. However, this requirement is frequently violated by real input data, such as DNA and protein sequences, that often differ in length due to insertions and deletions. It is also notable that performance in classification and regression is often improved by numerical encoding of amino acids, compared to the commonly used sparse encoding.
resultsThe software "Interpol" encodes amino acid sequences as numerical descriptor vectors using a database of currently 532 descriptors (mainly from AAindex), and normalizes sequences to uniform length with one of five linear or non-linear interpolation algorithms. Interpol is distributed with open source as platform independent R-package. It is typically used for preprocessing of amino acid sequences for classification or regression.
conclusionsThe functionality of Interpol widens the spectrum of machine learning methods that can be applied to biological sequences, and it will in many cases improve their performance in classification and regression.
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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.