ArticlePLoS computational biology2022
Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 33 citations in OpenAlex.
- SELEX-Based Aptamer Technologies for Toxin Analysis: Screening, Optimization, and Computational Assisted Design.Toxins · 2026Review
- Spiegelmer Aptamers: Innovative Approach in Breast Cancer.Breast cancer : basic and clinical research · 2026Review
- Sequence optimization of a DNA aptamer inhibiting COVID-19 infection guided by analysis of secondary structure distribution.Computational and structural biotechnology journal · 2026Article
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- Structure-enhanced deep learning accelerates aptamer selection for small molecule families like steroids.Briefings in bioinformatics · 2025Article
- Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects.Biosensors · 2025Review
- Designing the Future of Biosensing: Advances in Aptamer Discovery, Computational Modeling, and Diagnostic Applications.Biosensors · 2025Review
- Optimal sequencing depth for measuring the concentrations of molecular barcodes.Nucleic acids research · 2025Article
- Machine Learning for RNA Design: LEARNA.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Multiplexed Biomarker Detection Using DNA Payloads: Design, Assembly, and Analysis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Combination of Coevolutionary Information and Supervised Learning Enables Generation of Cyclic Peptide Inhibitors with Enhanced Potency from a Small Data Set.ACS central science · 2024Article
- Computational Frontiers in Aptamer-Based Nanomedicine for Precision Therapeutics: A Comprehensive Review.ACS omega · 2024Review
- Inference of annealed protein fitness landscapes with AnnealDCA.PLoS computational biology · 2024Article
- ACIDES: on-line monitoring of forward genetic screens for protein engineering.Nature communications · 2023Article
- Interpretable Machine Learning of Amino Acid Patterns in Proteins: A Statistical Ensemble Approach.Journal of chemical theory and computation · 2023Article
- Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.Briefings in bioinformatics · 2023Review
- Random and Natural Non-Coding RNA Have Similar Structural Motif Patterns but Differ in Bulge, Loop, and Bond Counts.Life (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann Machines (RBMs), an unsupervised two-layer neural network architecture, can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequence data at a given round can be used to predict the effects of selection at later rounds. Moreover, the parameters of the trained RBMs are interpretable and identify functional features contributing most to sequence fitness. To exploit the generative capabilities of RBMs, we introduce two different training protocols: one taking into account sequence counts, capable of identifying the few best binders, and another based on unique sequences only, generating more diverse binders. We then use RBMs model to generate novel aptamers with putative disruptive mutations or good binding properties, and validate the generated sequences with gel shift assay experiments. Finally, we compare the RBM's performance with different supervised learning approaches that include random forests and several deep neural network architectures.
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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.