ArticleMolecules (Basel, Switzerland)2017
Identification of DNA-protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information.
Article in Molecules (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- DNAreader: accurate prediction of DNA-binding residues in structured and disordered proteins using transformers and contrastive learning.Nucleic acids research · 2026Article
- TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.Communications biology · 2025Article
- Twenty years of advances in prediction of nucleic acid-binding residues in protein sequences.Briefings in bioinformatics · 2024Review
- HybridDBRpred: improved sequence-based prediction of DNA-binding amino acids using annotations from structured complexes and disordered proteins.Nucleic acids research · 2024Article
- Prediction of antifreeze proteins using machine learning.Scientific reports · 2022Article
- Application of DNA-Binding Protein Prediction Based on Graph Convolutional Network and Contact Map.BioMed research international · 2022Article
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- Research on RNA secondary structure predicting via bidirectional recurrent neural network.BMC bioinformatics · 2021Article
- A sequence-based multiple kernel model for identifying DNA-binding proteins.BMC bioinformatics · 2021Article
- Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks.Frontiers in genetics · 2021Article
- Use Chou's 5-Step Rule to Predict DNA-Binding Proteins with Evolutionary Information.BioMed research international · 2020Article
- Predicting ATP-Binding Cassette Transporters Using the Random Forest Method.Frontiers in genetics · 2020Article
- Article
- Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins.Frontiers in genetics · 2019Article
- Diagnosis of Brain Diseases via Multi-Scale Time-Series Model.Frontiers in neuroscience · 2019Article
- Special Protein Molecules Computational Identification.International journal of molecular sciences · 2018Article
- Multivariate Information Fusion With Fast Kernel Learning to Kernel Ridge Regression in Predicting LncRNA-Protein Interactions.Frontiers in genetics · 2018Article
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
DNA-protein interactions appear as pivotal roles in diverse biological procedures and are paramount for cell metabolism, while identifying them with computational means is a kind of prudent scenario in depleting in vitro and in vivo experimental charging. A variety of state-of-the-art investigations have been elucidated to improve the accuracy of the DNA-protein binding sites prediction. Nevertheless, structure-based approaches are limited under the condition without 3D information, and the predictive validity is still refinable. In this essay, we address a kind of competitive method called Multi-scale Local Average Blocks (MLAB) algorithm to solve this issue. Different from structure-based routes, MLAB exploits a strategy that not only extracts local evolutionary information from primary sequences, but also using predicts solvent accessibility. Moreover, the construction about predictors of DNA-protein binding sites wields an ensemble weighted sparse representation model with random under-sampling. To evaluate the performance of MLAB, we conduct comprehensive experiments of DNA-protein binding sites prediction. MLAB gives M C C of 0.392 , 0.315 , 0.439 and 0.245 on PDNA-543, PDNA-41, PDNA-316 and PDNA-52 datasets, respectively. It shows that MLAB gains advantages by comparing with other outstanding methods. M C C for our method is increased by at least 0.053 , 0.015 and 0.064 on PDNA-543, PDNA-41 and PDNA-316 datasets, respectively.
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.