ArticlePatterns (New York, N.Y.)2021
Accurate prediction of B-form/A-form DNA conformation propensity from primary sequence: A machine learning and free energy handshake.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Explainable random forest predictions of polyester biodegradability using high-throughput biodegradation data.Chemical science · 2026Article
- Prediction of enzyme function using an interpretable optimized ensemble learning framework.Chemical science · 2025Article
- Intrinsically disordered regions as facilitators of the transcription factor target search.Nature reviews. Genetics · 2025Review
- Near-infrared prediction of total phosphorus in leaves content in korla fragrant pear with growth period specificity via spectral modeling.Frontiers in plant science · 2025Article
- Non-canonical DNA structures in the human ribosomal DNA.Histochemistry and cell biology · 2023Review
- The molecular grammar of protein disorder guiding genome-binding locations.Nucleic acids research · 2023Article
- Elevated All-Cause Mortality among Overweight Older People: AI Predicts a High Normal Weight Is Optimal.Geriatrics (Basel, Switzerland) · 2022Article
- Developing Community Resources for Nucleic Acid Structures.Life (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA carries the genetic code of life, with different conformations associated with different biological functions. Predicting the conformation of DNA from its primary sequence, although desirable, is a challenging problem owing to the polymorphic nature of DNA. We have deployed a host of machine learning algorithms, including the popular state-of-the-art LightGBM (a gradient boosting model), for building prediction models. We used the nested cross-validation strategy to address the issues of "overfitting" and selection bias. This simultaneously provides an unbiased estimate of the generalization performance of a machine learning algorithm and allows us to tune the hyperparameters optimally. Furthermore, we built a secondary model based on SHAP (SHapley Additive exPlanations) that offers crucial insight into model interpretability. Our detailed model-building strategy and robust statistical validation protocols tackle the formidable challenge of working on small datasets, which is often the case in biological and medical data.
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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.