ArticleBMC bioinformatics2016
Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- TRP Channels Interactome as a Novel Therapeutic Target in Breast Cancer.Frontiers in oncology · 2021Pooled it
- Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.Bioinformatics advances · 2026Article
- DeepAptamer: Advancing high-affinity aptamer discovery with a hybrid deep learning model.Molecular therapy. Nucleic acids · 2025Article
- Article
- AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders.BMC bioinformatics · 2023Article
- DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design.PLoS computational biology · 2023Article
- Prediction of metal ion ligand binding residues by adding disorder value and propensity factors based on deep learning algorithm.Frontiers in genetics · 2022Article
- PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions.BMC bioinformatics · 2021Article
- In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.Molecular diversity · 2021Review
- AptaNet as a deep learning approach for aptamer-protein interaction prediction.Scientific reports · 2021Article
- SELEX-based DNA Aptamer Selection: A Perspective from the Advancement of Separation Techniques.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2021Review
- Article
- PPAI: a web server for predicting protein-aptamer interactions.BMC bioinformatics · 2020Article
- Article
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Authors and funding
5 authors.
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Abstract
backgroundAptamer-protein interacting pairs play a variety of physiological functions and therapeutic potentials in organisms. Rapidly and effectively predicting aptamer-protein interacting pairs is significant to design aptamers binding to certain interested proteins, which will give insight into understanding mechanisms of aptamer-protein interacting pairs and developing aptamer-based therapies.
resultsIn this study, an ensemble method is presented to predict aptamer-protein interacting pairs with hybrid features. The features for aptamers are extracted from Pseudo K-tuple Nucleotide Composition (PseKNC) while the features for proteins incorporate Discrete Cosine Transformation (DCT), disorder information, and bi-gram Position Specific Scoring Matrix (PSSM). We investigate predictive capabilities of various feature spaces. The proposed ensemble method obtains the best performance with Youden's Index of 0.380, using the hybrid feature space of PseKNC, DCT, bi-gram PSSM, and disorder information by 10-fold cross validation. The Relief-Incremental Feature Selection (IFS) method is adopted to obtain the optimal feature set. Based on the optimal feature set, the proposed method achieves a balanced performance with a sensitivity of 0.753 and a specificity of 0.725 on the training dataset, which indicates that this method can solve the imbalanced data problem effectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous study with a sensitivity of 0.738 and a Youden's Index of 0.451.
conclusionsThese results suggest that the proposed method can be a potential candidate for aptamer-protein interacting pair prediction, which may contribute to finding novel aptamer-protein interacting pairs and understanding the relationship between aptamers and proteins.
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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.