ArticleScientific reports2021
AptaNet as a deep learning approach for aptamer-protein interaction prediction.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Molecular Engineering of Aptamers for Glioblastoma Therapy: From Simple Antagonists to AI-Driven Approaches, a Narrative Review.International journal of molecular sciences · 2026Review
- Selection of Novel DNA Aptamers against HPV16 Biomarker, E6: AnACS omega · 2026Article
- Are we there yet with XNA aptamers?RSC advances · 2026Review
- Aptamers targeting immune checkpoints for tumor immunotherapy: a comprehensive review.Frontiers in oncology · 2026Review
- Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.Bioinformatics advances · 2026Article
- Aptamers and aptamer-drug conjugates as synthetic immune modulators for cancer immunotherapy.Frontiers in immunology · 2026Review
- Structure-enhanced deep learning accelerates aptamer selection for small molecule families like steroids.Briefings in bioinformatics · 2025Article
- Designing the Future of Biosensing: Advances in Aptamer Discovery, Computational Modeling, and Diagnostic Applications.Biosensors · 2025Review
- Aptamers in Drug Delivery Development.Materials today (Kidlington, England) · 2025Article
- Aptamers Targeting Immune Checkpoints for Tumor Immunotherapy.Pharmaceutics · 2025Review
- DeepAptamer: Advancing high-affinity aptamer discovery with a hybrid deep learning model.Molecular therapy. Nucleic acids · 2025Article
- AptaDiff: de novo design and optimization of aptamers based on diffusion models.Briefings in bioinformatics · 2024Article
- Current developments of SELEX technologies and prospects in the aptamer selection with clinical applications.Journal, genetic engineering & biotechnology · 2024Review
- Nucleic acid-based wearable and implantable electrochemical sensors.Chemical Society reviews · 2024Review
- Computational Frontiers in Aptamer-Based Nanomedicine for Precision Therapeutics: A Comprehensive Review.ACS omega · 2024Review
- APIPred: An XGBoost-Based Method for Predicting Aptamer-Protein Interactions.Journal of chemical information and modeling · 2024Article
- HormoNet: a deep learning approach for hormone-drug interaction prediction.BMC bioinformatics · 2024Article
- AptaDB: a comprehensive database integrating aptamer-target interactions.RNA (New York, N.Y.) · 2024Article
- Article
- AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders.BMC bioinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aptamers are short oligonucleotides (DNA/RNA) or peptide molecules that can selectively bind to their specific targets with high specificity and affinity. As a powerful new class of amino acid ligands, aptamers have high potentials in biosensing, therapeutic, and diagnostic fields. Here, we present AptaNet-a new deep neural network-to predict the aptamer-protein interaction pairs by integrating features derived from both aptamers and the target proteins. Aptamers were encoded by using two different strategies, including k-mer and reverse complement k-mer frequency. Amino acid composition (AAC) and pseudo amino acid composition (PseAAC) were applied to represent target information using 24 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied a neighborhood cleaning algorithm. The predictor was constructed based on a deep neural network, and optimal features were selected using the random forest algorithm. As a result, 99.79% accuracy was achieved for the training dataset, and 91.38% accuracy was obtained for the testing dataset. AptaNet achieved high performance on our constructed aptamer-protein benchmark dataset. The results indicate that AptaNet can help identify novel aptamer-protein interacting pairs and build more-efficient insights into the relationship between aptamers and proteins. Our benchmark dataset and the source codes for AptaNet are available in: https://github.com/nedaemami/AptaNet .
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.