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

Wang, Hao; Liu, Chuyao; Deng, Lei

doi:10.1038/s41598-018-32511-1

Cited by 82 publications

(50 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, we developed a deep learning framework called 4mcDeep-CBI to identify the 4mC sites. Deep learning related methods are widely used in hot spots prediction of proteinprotein interfaces Wang et al, 2018;Deng et al, 2019;Liu et al, 2019), but we have not found any work with deep learning in 4mC sites prediction, and all previous studies have used SVM machine learning methods. This work is the first study of 4mC sites using deep learning.…”

Section: Introductionmentioning

confidence: 91%

A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

2020

View full text Add to dashboard Cite

Motivation: N4-methylcytosine (4mC) plays an important role in host defense and transcriptional regulation. Accurate identification of 4mc sites provides a more comprehensive understanding of its biological effects. At present, the traditional machine learning algorithms are used in the research on 4mC sites prediction, but the complexity of the algorithms is relatively high, which is not suitable for the processing of large data sets, and the accuracy of prediction needs to be improved. Therefore, it is necessary to develop a new and effective method to accurately identify 4mC sites.Results: In this work, we found a large number of 4mC sites and non 4mC sites of Caenorhabditis elegans (C. elegans) from the latest MethSMRT website, which greatly expanded the dataset of C. elegans, and developed a hybrid deep neural network framework named 4mcDeep-CBI, aiming to identify 4mC sites. In order to obtain the high latitude information of the feature, we input the preliminary extracted features into the Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory network (BLSTM) to generate advanced features. Taking the advanced features as algorithm input, we have proposed an integrated algorithm to improve feature representation. Experimental results on large new dataset show that the proposed predictor is able to achieve generally better performance in identifying 4mC sites as compared to the state-of-art predictor. Notably, this is the first study of identifying 4mC sites using deep neural network. Moreover, our model runs much faster than the state-of-art predictor.

show abstract

Section: Introductionmentioning

confidence: 91%

A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

2020

View full text Add to dashboard Cite

show abstract

“…XGBoost is widely used by data scientists in multiple applications and has provided advanced results [58,59]. The training set after feature extraction and SMOTE…”

Section: Extreme Gradient Boosting Algorithmmentioning

confidence: 99%

SXGBsite: Prediction of Protein–Ligand Binding Sites Using Sequence Information and Extreme Gradient Boosting

Zhao

2019

Genes

View full text Add to dashboard Cite

The prediction of protein–ligand binding sites is important in drug discovery and drug design. Protein–ligand binding site prediction computational methods are inexpensive and fast compared with experimental methods. This paper proposes a new computational method, SXGBsite, which includes the synthetic minority over-sampling technique (SMOTE) and the Extreme Gradient Boosting (XGBoost). SXGBsite uses the position-specific scoring matrix discrete cosine transform (PSSM-DCT) and predicted solvent accessibility (PSA) to extract features containing sequence information. A new balanced dataset was generated by SMOTE to improve classifier performance, and a prediction model was constructed using XGBoost. The parallel computing and regularization techniques enabled high-quality and fast predictions and mitigated overfitting caused by SMOTE. An evaluation using 12 different types of ligand binding site independent test sets showed that SXGBsite performs similarly to the existing methods on eight of the independent test sets with a faster computation time. SXGBsite may be applied as a complement to biological experiments.

show abstract

“…HaoWang, ChuyaoLiu & LeiDeng et.al [5] applied XGBoost and built a model called PredHS2 for detection of hot spots. Hotspots tiny segments of protein-protein interface residues contributing majority of the binding free energy.…”

Section: Related Workmentioning

confidence: 99%

Accurate Liver Disease Prediction with Extreme Gradient Boosting

Murty¹,

Kumar²

2019

IJEAT

View full text Add to dashboard Cite

Machine learning is used extensively in medical diagnosis to predict the existence of diseases. Existing classification algorithms are frequently used for automatic detection of diseases. But most of the times, they do not give 100% accurate results. Boosting techniques are often used in Machine learning to get maximum classification accuracy. Though several boosting techniques are in place but the XGBoost algorithm is doing extremely well for some selected data sets. Building an XGBoost model is simple but improving the model by tuning the parameters is a challenging task. There are many parameters to the XGBoost algorithm and deciding what set of parameters to tune and the ideal values of these parameters is a cumbersome and time taking task. We, in this paper, tuned the XGBoost model for the first time for Liver disease prediction and got 99% accuracy by tuning some of the hyper parameters. It is observed that the model proposed by us exhibited highest classification accuracy compared to all other models built till now by machine learning researchers and some regularly used algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision tree, Random Belief Networks, Alternating Decision Trees (ADT) experimented by us.

show abstract

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

Cited by 82 publications

References 68 publications

A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

SXGBsite: Prediction of Protein–Ligand Binding Sites Using Sequence Information and Extreme Gradient Boosting

Accurate Liver Disease Prediction with Extreme Gradient Boosting

Contact Info

Product

Resources

About