ArticleJournal of structural and functional genomics2011
Prediction of metalloproteinase family based on the concept of Chou's pseudo amino acid composition using a machine learning approach.
Article in Journal of structural and functional genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 113 citations in OpenAlex.
- Using several pseudo amino acid composition types and different machine learning algorithms to classify and predict archaeal phospholipases.Molecular biology research communications · 2023Article
- Some illuminating remarks on molecular genetics and genomics as well as drug development.Molecular genetics and genomics : MGG · 2020Review
- Article
- iRSpot-Pse6NC: Identifying recombination spots inInternational journal of biological sciences · 2018Article
- iACP: a sequence-based tool for identifying anticancer peptides.Oncotarget · 2016Article
- repRNA: a web server for generating various feature vectors of RNA sequences.Molecular genetics and genomics : MGG · 2016Article
- iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.Molecular genetics and genomics : MGG · 2016Article
- Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis.Molecular genetics and genomics : MGG · 2015Article
- Identification of real microRNA precursors with a pseudo structure status composition approach.PloS one · 2015Article
- Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou's pseudo amino acid composition.International journal of molecular sciences · 2014Article
- iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.International journal of molecular sciences · 2014Article
- PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets.International journal of molecular sciences · 2014Article
- Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection.Bioinformatics (Oxford, England) · 2014Article
- iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components.International journal of molecular sciences · 2014Article
- iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.BioMed research international · 2014Article
- iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels.BioMed research international · 2014Article
- iNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid composition.PloS one · 2014Article
- Article
- iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition.Nucleic acids research · 2013Article
- A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.The Journal of membrane biology · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Matrix metalloproteinase (MMPs) and disintegrin and metalloprotease (ADAMs) belong to the zinc-dependent metalloproteinase family of proteins. These proteins participate in various physiological and pathological states. Thus, prediction of these proteins using amino acid sequence would be helpful. We have developed a method to predict these proteins based on the features derived from Chou's pseudo amino acid composition (PseAAC) server and support vector machine (SVM) as a powerful machine learning approach. With this method, for ADAMs and MMPs families, an overall accuracy and Matthew's correlation coefficient (MCC) of 95.89 and 0.90% were achieved respectively. Furthermore, the method is able to predict two major subclasses of MMP family; Furin-activated secreted MMPs and Type II trans-membrane; with MCC of 0.89 and 0.91%, respectively. The overall accuracy for Furin-activated secreted MMPs and Type II trans-membrane was 98.18 and 99.07, respectively. Our data demonstrates an effective classification of Metalloproteinase family based on the concept of PseAAC and SVM.
Indexed as
Identifiers
What OpenQuestion holds
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.