ArticlePloS one2017
Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 50 citations in OpenAlex.
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- Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia.Informatics in medicine unlocked · 2022Article
- Computationally identifying hot spots in protein-DNA binding interfaces using an ensemble approach.BMC bioinformatics · 2020Article
- 6mA-RicePred: A Method for Identifying DNAFrontiers in plant science · 2020Article
- Computational identification of N6-methyladenosine sites in multiple tissues of mammals.Computational and structural biotechnology journal · 2020Article
- Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network.BMC bioinformatics · 2019Article
- Prediction of Enzyme Function Based on Three Parallel Deep CNN and Amino Acid Mutation.International journal of molecular sciences · 2019Article
- Genome-wide analysis of epigenetic dynamics across human developmental stages and tissues.BMC genomics · 2019Article
- A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features.Frontiers in bioengineering and biotechnology · 2019Article
- PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.BMC bioinformatics · 2018Article
- Integrating Multiple Interaction Networks for Gene Function Inference.Molecules (Basel, Switzerland) · 2018Article
- Accurate prediction of protein-lncRNA interactions by diffusion and HeteSim features across heterogeneous network.BMC bioinformatics · 2018Article
- Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting.Scientific reports · 2018Article
- Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data.Frontiers in pharmacology · 2018Review
- A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave.BioMed research international · 2018Article
- Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA inFrontiers in microbiology · 2018Article
- ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network.Molecules (Basel, Switzerland) · 2017Article
- Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.Molecules (Basel, Switzerland) · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single amino acid variations (SAVs) potentially alter biological functions, including causing diseases or natural differences between individuals. Identifying the relationship between a SAV and certain disease provides the starting point for understanding the underlying mechanisms of specific associations, and can help further prevention and diagnosis of inherited disease.We propose PredSAV, a computational method that can effectively predict how likely SAVs are to be associated with disease by incorporating gradient tree boosting (GTB) algorithm and optimally selected neighborhood features. A two-step feature selection approach is used to explore the most relevant and informative neighborhood properties that contribute to the prediction of disease association of SAVs across a wide range of sequence and structural features, especially some novel structural neighborhood features. In cross-validation experiments on the benchmark dataset, PredSAV achieves promising performances with an AUC score of 0.908 and a specificity of 0.838, which are significantly better than that of the other existing methods. Furthermore, we validate the capability of our proposed method by an independent test and gain a competitive advantage as a result. PredSAV, which combines gradient tree boosting with optimally selected neighborhood features, can return reliable predictions in distinguishing between disease-associated and neutral variants. Compared with existing methods, PredSAV shows improved specificity as well as increased overall performance.
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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.