ArticleBioMed research international2016
Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm.
Article in BioMed research international, 2016. 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, 25 citations in OpenAlex.
- A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.PLoS computational biology · 2021Article
- TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree.Journal of computer-aided molecular design · 2020Article
- Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.Cancer gene therapy · 2020Article
- Identification of the copy number variant biomarkers for breast cancer subtypes.Molecular genetics and genomics : MGG · 2019Article
- Article
- Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection.Molecular genetics and genomics : MGG · 2018Article
- Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV.BioMed research international · 2018Article
- Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets.Oncotarget · 2017Article
- Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways.PloS one · 2017Article
- Prediction of phosphothreonine sites in human proteins by fusing different features.Scientific reports · 2016Article
- Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer.BioMed research international · 2016Article
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of biochemistry and molecular biology has revealed an increasingly important role of compounds in several biological processes. Like the aptamer-protein interaction, aptamer-compound interaction attracts increasing attention. However, it is time-consuming to select proper aptamers against compounds using traditional methods, such as exponential enrichment. Thus, there is an urgent need to design effective computational methods for searching effective aptamers against compounds. This study attempted to extract important features for aptamer-compound interactions using feature selection methods, such as Maximum Relevance Minimum Redundancy, as well as incremental feature selection. Each aptamer-compound pair was represented by properties derived from the aptamer and compound, including frequencies of single nucleotides and dinucleotides for the aptamer, as well as the constitutional, electrostatic, quantum-chemical, and space conformational descriptors of the compounds. As a result, some important features were obtained. To confirm the importance of the obtained features, we further discussed the associations between them and aptamer-compound interactions. Simultaneously, an optimal prediction model based on the nearest neighbor algorithm was built to identify aptamer-compound interactions, which has the potential to be a useful tool for the identification of novel aptamer-compound interactions. The program is available upon the request.
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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.