ArticleScientific reports2016
Sorting protein decoys by machine-learning-to-rank.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- MUfoldQA_G: High-accuracy protein model QA via retraining and transformation.Computational and structural biotechnology journal · 2021Article
- Decoy selection for protein structure prediction via extreme gradient boosting and ranking.BMC bioinformatics · 2020Article
- Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.Biomolecules · 2019Article
- Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction.Molecules (Basel, Switzerland) · 2019Article
- Validation and quality assessment of macromolecular structures using complex network analysis.Scientific reports · 2019Article
- RRCRank: a fusion method using rank strategy for residue-residue contact prediction.BMC bioinformatics · 2017Article
- An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences.Acta crystallographica. Section D, Structural biology · 2017Review
- MQAPRank: improved global protein model quality assessment by learning-to-rank.BMC bioinformatics · 2017Article
- Two New Heuristic Methods for Protein Model Quality Assessment.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Much progress has been made in Protein structure prediction during the last few decades. As the predicted models can span a broad range of accuracy spectrum, the accuracy of quality estimation becomes one of the key elements of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, and these methods could be roughly divided into three categories: the single-model methods, clustering-based methods and quasi single-model methods. In this study, we develop a single-model method MQAPRank based on the learning-to-rank algorithm firstly, and then implement a quasi single-model method Quasi-MQAPRank. The proposed methods are benchmarked on the 3DRobot and CASP11 dataset. The five-fold cross-validation on the 3DRobot dataset shows the proposed single model method outperforms other methods whose outputs are taken as features of the proposed method, and the quasi single-model method can further enhance the performance. On the CASP11 dataset, the proposed methods also perform well compared with other leading methods in corresponding categories. In particular, the Quasi-MQAPRank method achieves a considerable performance on the CASP11 Best150 dataset.
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.