Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties

Pan, Yuliang; Liu, Diwei; Deng, Lei

doi:10.1371/journal.pone.0179314

Cited by 45 publications

(23 citation statements)

References 84 publications

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“…We first select three features from the Top-50 features as the initial feature combinations, which is similar to the process in HEP 31 . Then we add correlation features by using sequential forward selection (SFS) method 38 . In the SFS method, features from the Top-500 features are sequentially added to the initial feature combinations until the ranking criterion R c no longer increased.…”

Section: Methodsmentioning

confidence: 99%

Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting

Wang

Liu

Deng

2018

Sci Rep

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Identification of hot spots, a small portion of protein-protein interface residues that contribute the majority of the binding free energy, can provide crucial information for understanding the function of proteins and studying their interactions. Based on our previous method (PredHS), we propose a new computational approach, PredHS2, that can further improve the accuracy of predicting hot spots at protein-protein interfaces. Firstly we build a new training dataset of 313 alanine-mutated interface residues extracted from 34 protein complexes. Then we generate a wide variety of 600 sequence, structure, exposure and energy features, together with Euclidean and Voronoi neighborhood properties. To remove redundant and irrelevant information, we select a set of 26 optimal features utilizing a two-step feature selection method, which consist of a minimum Redundancy Maximum Relevance (mRMR) procedure and a sequential forward selection process. Based on the selected 26 features, we use Extreme Gradient Boosting (XGBoost) to build our prediction model. Performance of our PredHS2 approach outperforms other machine learning algorithms and other state-of-the-art hot spot prediction methods on the training dataset and the independent test set (BID) respectively. Several novel features, such as solvent exposure characteristics, second structure features and disorder scores, are found to be more effective in discriminating hot spots. Moreover, the update of the training dataset and the new feature selection and classification algorithms play a vital role in improving the prediction quality.

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Section: Methodsmentioning

confidence: 99%

Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting

Wang

Liu

Deng

2018

Sci Rep

Self Cite

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“…K-fold cross-validation (Chou and Zhang, 1995 ; Kohavi, 1995 ; Zhang et al, 2012a , b , 2015 ; Liu et al, 2015a ; Chen X. et al, 2016 ; Li et al, 2016 ; Luo et al, 2016 ; Chen et al, 2017b , 2018a , b ; Pan et al, 2017a ; Xu et al, 2017 ; He et al, 2018 ) is one of the widely used approach to examine the ability of prediction model, and other approaches: independent dataset test and jackknife test (Chou and Shen, 2008 ) are also used in many applications. To reduce the computational cost, 10-fold cross validation was used to examine each model for its effectiveness in identifying ncDNA sequences.…”

Section: Methodsmentioning

confidence: 99%

Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA in Saccharomyces cerevisiae

Zeng

et al. 2018

Front. Microbiol.

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With the rapid development of high-speed sequencing technologies and the implementation of many whole genome sequencing project, research in the genomics is advancing from genome sequencing to genome synthesis. Synthetic biology technologies such as DNA-based molecular assemblies, genome editing technology, directional evolution technology and DNA storage technology, and other cutting-edge technologies emerge in succession. Especially the rapid growth and development of DNA assembly technology may greatly push forward the success of artificial life. Meanwhile, DNA assembly technology needs a large number of target sequences of known information as data support. Non-coding DNA (ncDNA) sequences occupy most of the organism genomes, thus accurate recognizing of them is necessary. Although experimental methods have been proposed to detect ncDNA sequences, they are expensive for performing genome wide detections. Thus, it is necessary to develop machine-learning methods for predicting non-coding DNA sequences. In this study, we collected the ncDNA benchmark dataset of Saccharomyces cerevisiae and reported a support vector machine-based predictor, called Sc-ncDNAPred, for predicting ncDNA sequences. The optimal feature extraction strategy was selected from a group included mononucleotide, dimer, trimer, tetramer, pentamer, and hexamer, using support vector machine learning method. Sc-ncDNAPred achieved an overall accuracy of 0.98. For the convenience of users, an online web-server has been built at: http://server.malab.cn/Sc_ncDNAPred/index.jsp.

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“…According to PSSM, 20 features were extracted using the following formula [ 27 , 28 , 29 ]:

where

indicates the average score of the amino acid residue at each position of the sequence S as mutated by the amino acid residue j during evolution.…”

Section: Methodsmentioning

confidence: 99%

Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods

Han

et al. 2017

Molecules

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DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

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Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties

Cited by 45 publications

References 84 publications

Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting

Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting

Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA in Saccharomyces cerevisiae

Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods

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