ReviewFrontiers in genetics2021
Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm.
Review in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Construction of an associative model for prolonged intensive care unit stay in sepsis patients combined with myocardial injury.Clinics (Sao Paulo, Brazil) · 2026Article
- Integrating machine learning with SHAP to uncover multi-tissue molecular signatures in Osteoarthritis progression.PloS one · 2026Article
- Development of an explainable machine learning asthma prediction model using serum brominated flame retardants in a national population.Clinical and experimental medicine · 2025Article
- Radiomic study of common sellar region lesions differentiation in magnetic resonance imaging based on multi-classification machine learning model.BMC medical imaging · 2025Article
- Multimodal machine learning-based model for differentiating nontuberculous mycobacteria fromFrontiers in public health · 2025Article
- Development and validation of AI/ML derived splice-switching oligonucleotides.Molecular systems biology · 2024Article
- Construction and experimental validation of a novel ferroptosis-related gene signature for myelodysplastic syndromes.Immunity, inflammation and disease · 2024Article
- Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms.Scientific reports · 2023Article
- Predicting the retention time of Synthetic Cannabinoids using a combinatorial QSAR approach.Heliyon · 2023Article
- Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences.mSystems · 2022Article
- DBP-iDWT: Improving DNA-Binding Proteins Prediction Using Multi-Perspective Evolutionary Profile and Discrete Wavelet Transform.Computational intelligence and neuroscience · 2022Article
- Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma.Frontiers in immunology · 2022Article
- Comparative Analysis on Alignment-Based and Pretrained Feature Representations for the Identification of DNA-Binding Proteins.Computational and mathematical methods in medicine · 2022Article
- Atherogenic index of plasma and severe headaches or migraines risk in US adults: a population-based cross-sectional analysis from NHANES 1999-2004.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods.
Indexed as
Identifiers
What Socratic 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 Socratic graph.