HBPred: a tool to identify growth hormone-binding proteins

Tang, Hua; Zhao, Yue; Ping, Zou; Zhang, Chunmei; Chen, Rong; Huang, Po Hsien; Lin, Hao

doi:10.7150/ijbs.24174

Cited by 168 publications

(88 citation statements)

References 75 publications

(61 reference statements)

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“…In order to distinguish the contribution of different features to the prediction model. To analyze these feature vectors, F-score method (Chen W. et al, 2016 ; Jia and He, 2016 ; Tang et al, 2016 , 2018 ; He and Jia, 2017 ) was adopted to rank the feature, in this study. The F-score value of the i -th feature is defined as:

where

and

are the average values of the i -th feature in whole, ncDNA and cDNA datasets, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…To evaluate the prediction performance of the models, five classic metrics were computed (Chou, 2001 ; Qiu et al, 2015 , 2016 ; Liu et al, 2017 ; Pan et al, 2017b ; Zhang et al, 2017a ; Tang et al, 2018 ; Yang et al, 2018 ), including sensitivity (Sn), specificity (Sp), accuracy (Acc), Matthew correlation coefficient (MCC), and the receiver operating characteristic (ROC). These measurements were defined as:

In these expressions, N + and N − are the total number of ncDNA and cDNA samples, respectively, while

and

are respectively the number of ncDNA samples incorrectly predicted as cDNA samples, and the number of cDNA samples incorrectly predicted as ncDNA samples.…”

Section: Methodsmentioning

confidence: 99%

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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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where

and

are the average values of the i -th feature in whole, ncDNA and cDNA datasets, respectively.…”

Section: Methodsmentioning

confidence: 99%

In these expressions, N + and N − are the total number of ncDNA and cDNA samples, respectively, while

and

are respectively the number of ncDNA samples incorrectly predicted as cDNA samples, and the number of cDNA samples incorrectly predicted as ncDNA samples.…”

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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“…The purpose of dimensionality reduction or feature selection is to reduce the computational time and complexity of the prediction model, and also to provide more insights into the data abundance (Basith, et al, 2020;Govindaraj, et al, 2020;He, et al, 2018;Jing, et al, 2019;Kang, et al, 2019;Li, et al, 2020;Liu, et al, 2019;Manavalan, et al, 2018;Shi, et al, 2019;Su, et al, 2020;Tang, et al, 2018;Xiong, et al, 2012;Xiong, et al, 2019;. It is indispensable to reduce dimensionality to remove redundant features so that we can reserve the important ones.…”

Section: Feature Selectionmentioning

confidence: 99%

T4SE-XGB: interpretable sequence-based prediction of type IV secreted effectors using eXtreme gradient boosting algorithm

Chen

Wang

Chu

et al. 2020

Preprint

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Type IV secreted effectors (T4SEs) can be translocated into the cytosol of host cells via type IV secretion system (T4SS) and cause diseases. However, experimental approaches to identify T4SEs are time-and resource-consuming, and the existing computational tools based on machine learning techniques have some obvious limitations such as the lack of interpretability in the prediction models. In this study, we proposed a new model, T4SE-XGB, which uses the eXtreme gradient boosting (XGBoost) algorithm for accurate identification of type IV effectors based on optimal protein sequence features. After trying 20 different features, the best result achieved when all features were fed into XGBoost by the 5-fold cross validation compared with different machine learning methods. Then, the ReliefF algorithm was adopted to optimize feature vectors and got final 1100 features for our dataset which obviously improved the model performance. T4SE-XGB exhibited highest predictive performance on the independent test set and clearly outperforms other recent prediction tools. What's more, the SHAP method was used to interpret the contribution of features to model predictions. The identification of key features can contribute to an improved understanding of multifactorial contributors to host-pathogen interactions and bacterial pathogenesis. In addition to type IV effector prediction, we believe that the proposed framework composed of model construction and model interpretation can provide more instructive guidance for further research of developing novel computational methods and mechanism exploration of biological problems. The data and code for this study can be found at https://github.com/CT001002/T4SE-XGB.

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“…Here, it can be viewed as any combination of two amino acids. Since there are 20 kinds of amino acids, a total of 400 dipeptide compositions are possible [49,50]. Dipeptide composition is obtained by calculating the ratio of the number of occurrences of dipeptides in the sequence to the sequence length.…”

Section: Dipeptide Compositionmentioning

confidence: 99%

PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction

et al. 2019

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Interactions between proteins and DNAs play essential roles in many biological processes. DNA binding proteins can be classified into two categories. Double-stranded DNA-binding proteins (DSBs) bind to double-stranded DNA and are involved in a series of cell functions such as gene expression and regulation. Single-stranded DNA-binding proteins (SSBs) are necessary for DNA replication, recombination, and repair and are responsible for binding to the single-stranded DNA. Therefore, the effective classification of DNA-binding proteins is helpful for functional annotations of proteins. In this work, we propose PredPSD, a computational method based on sequence information that accurately predicts SSBs and DSBs. It introduces three novel feature extraction algorithms. In particular, we use the autocross-covariance (ACC) transformation to transform feature matrices into fixed-length vectors. Then, we put the optimal feature subset obtained by the minimal-redundancy-maximal-relevance criterion (mRMR) feature selection algorithm into the gradient tree boosting (GTB). In 10-fold cross-validation based on a benchmark dataset, PredPSD achieves promising performances with an AUC score of 0.956 and an accuracy of 0.912, which are better than those of existing methods. Moreover, our method has significantly improved the prediction accuracy in independent testing. The experimental results show that PredPSD can significantly recognize the binding specificity and differentiate DSBs and SSBs.

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HBPred: a tool to identify growth hormone-binding proteins

Cited by 168 publications

References 75 publications

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

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

T4SE-XGB: interpretable sequence-based prediction of type IV secreted effectors using eXtreme gradient boosting algorithm

PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction

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