ArticleBMC bioinformatics2020
PPAI: a web server for predicting protein-aptamer interactions.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unveiling the state of the art: a systematic review and meta-analysis of paper-based microfluidic devices.Frontiers in bioengineering and biotechnology · 2024Pooled it
- Nucleic acid aptamers: new methods for selection, target validation, molecular diagnostics and therapeutics.Signal transduction and targeted therapy · 2026Review
- Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.Bioinformatics advances · 2026Article
- APIPred: An XGBoost-Based Method for Predicting Aptamer-Protein Interactions.Journal of chemical information and modeling · 2024Article
- AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders.BMC bioinformatics · 2023Article
- Design and Prediction of Aptamers Assisted by In Silico Methods.Biomedicines · 2023Review
- In vitro selection of aptamers and their applications.Nature reviews. Methods primers · 2023Article
- FEPS: A Tool for Feature Extraction from Protein Sequence.Methods in molecular biology (Clifton, N.J.) · 2022Article
- An analytical study on the identification of N-linked glycosylation sites using machine learning model.PeerJ. Computer science · 2022Article
- Recent Advances in Micro/Nanomaterial-Based Aptamer Selection Strategies.Molecules (Basel, Switzerland) · 2021Review
- AptaNet as a deep learning approach for aptamer-protein interaction prediction.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
backgroundThe interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies.
resultsIn this study, a novel web server named PPAI is developed to predict aptamers and protein-aptamer interactions with key sequence features of proteins/aptamers and a machine learning framework integrated adaboost and random forest. A new method for extracting several key sequence features of both proteins and aptamers is presented, where the features for proteins are extracted from amino acid composition, pseudo-amino acid composition, grouped amino acid composition, C/T/D composition and sequence-order-coupling number, while the features for aptamers are extracted from nucleotide composition, pseudo-nucleotide composition (PseKNC) and normalized Moreau-Broto autocorrelation coefficient. On the basis of these feature sets and balanced the samples with SMOTE algorithm, we validate the performance of PPAI by the independent test set. The results demonstrate that the Area Under Curve (AUC) is 0.907 for prediction of aptamer, while the AUC reaches 0.871 for prediction of protein-aptamer interactions.
conclusionThese results indicate that PPAI can query aptamers and proteins, predict aptamers and predict protein-aptamer interactions in batch mode precisely and efficiently, which would be a novel bioinformatics tool for the research of protein-aptamer interactions. PPAI web-server is freely available at http://39.96.85.9/PPAI.
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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.