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Author

Yi-Heng Zhu

Other affiliations: Jiangnan University
Bio: Yi-Heng Zhu is an academic researcher from Nanjing University of Science and Technology. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 4, co-authored 13 publications receiving 65 citations. Previous affiliations of Yi-Heng Zhu include Jiangnan University.

Papers
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Journal ArticleDOI
TL;DR: This paper establishes a novel computational method, named TargetDBP, for accurately targeting DBPs from primary sequences, and constructs a new gold-standard and non-redundant benchmark dataset from PDB database to evaluate and compare the proposed targetDBP with other existing predictors.
Abstract: Accurately identifying DNA-binding proteins (DBPs) from protein sequence information is an important but challenging task for protein function annotations. In this paper, we establish a novel computational method, named TargetDBP, for accurately targeting DBPs from primary sequences. In TargetDBP, four single-view features, i.e., AAC (Amino Acid Composition), PsePSSM (Pseudo Position-Specific Scoring Matrix), PsePRSA (Pseudo Predicted Relative Solvent Accessibility), and PsePPDBS (Pseudo Predicted Probabilities of DNA-Binding Sites), are first extracted to represent different base features, respectively. Second, differential evolution algorithm is employed to learn the weights of four base features. Using the learned weights, we weightedly combine these base features to form the original super feature. An excellent subset of the super feature is then selected by using a suitable feature selection algorithm SVM-REF+CBR (Support Vector Machine Recursive Feature Elimination with Correlation Bias Reduction). Finally, the prediction model is learned via using support vector machine on the selected feature subset. We also construct a new gold-standard and non-redundant benchmark dataset from PDB database to evaluate and compare the proposed TargetDBP with other existing predictors. On this new dataset, TargetDBP can achieve higher performance than other state-of-the-art predictors. The TargetDBP web server and datasets are freely available at http://csbio.njust.edu.cn/bioinf/targetdbp/ for academic use.

40 citations

Journal ArticleDOI
TL;DR: A two-stage imbalanced learning algorithm, called ensembled hyperplane-distance-based support vector machines (E-HDSVM), to improve the prediction performance of protein-DNA binding sites and an enhanced AdaBoost algorithm to ensemble multiple trained SVMs, which overcomes the overfitting problem.
Abstract: Accurate identification of protein–DNA binding sites is significant for both understanding protein function and drug design. Machine-learning-based methods have been extensively used for the predic...

34 citations

Journal ArticleDOI
TL;DR: A new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity is developed, which improves crystallization recognition by incorporating sequence-order information with solvent accessibility of residues.
Abstract: X-ray crystallography is the major approach for determining atomic-level protein structures. Because not all proteins can be easily crystallized, accurate prediction of protein crystallization propensity provides critical help in guiding experimental design and improving the success rate of X-ray crystallography experiments. This study has developed a new machine-learning-based pipeline that uses a newly developed deep-cascade forest (DCF) model with multiple types of sequence-based features to predict protein crystallization propensity. Based on the developed pipeline, two new protein crystallization propensity predictors, denoted as DCFCrystal and MDCFCrystal, have been implemented. DCFCrystal is a multistage predictor that can estimate the success propensities of the three individual steps (production of protein material, purification and production of crystals) in the protein crystallization process. MDCFCrystal is a single-stage predictor that aims to estimate the probability that a protein will pass through the entire crystallization process. Moreover, DCFCrystal is designed for general proteins, whereas MDCFCrystal is specially designed for membrane proteins, which are notoriously difficult to crystalize. DCFCrystal and MDCFCrystal were separately tested on two benchmark datasets consisting of 12 289 and 950 proteins, respectively, with known crystallization results from various experimental records. The experimental results demonstrated that DCFCrystal and MDCFCrystal increased the value of Matthew's correlation coefficient by 199.7% and 77.8%, respectively, compared to the best of other state-of-the-art protein crystallization propensity predictors. Detailed analyses show that the major advantages of DCFCrystal and MDCFCrystal lie in the efficiency of the DCF model and the sensitivity of the sequence-based features used, especially the newly designed pseudo-predicted hybrid solvent accessibility (PsePHSA) feature, which improves crystallization recognition by incorporating sequence-order information with solvent accessibility of residues. Meanwhile, the new crystal-dataset constructions help to train the models with more comprehensive crystallization knowledge.

16 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information.
Abstract: Transmembrane proteins have critical biological functions and play a role in a multitude of cellular processes including cell signaling, transport of molecules and ions across membranes. Approximately 60% of transmembrane proteins are considered as drug targets. Missense mutations in such proteins can lead to many diverse diseases and disorders, such as neurodegenerative diseases and cystic fibrosis. However, there are limited studies on mutations in transmembrane proteins. In this work, we first design a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information. Then, we propose a new mutation prediction algorithm (cascade XGBoost) by leveraging the idea learned from consensus predictors and gcForest. Multi-level experiments illustrate the effectiveness of WAPSSM and cascade XGBoost algorithms. Finally, based on WAPSSM and other three types of features, in combination with the cascade XGBoost algorithm, we develop a new transmembrane protein mutation predictor, named MutTMPredictor. We benchmark the performance of MutTMPredictor against several existing predictors on seven datasets. On the 546 mutations dataset, MutTMPredictor achieves the accuracy (ACC) of 0.9661 and the Matthew’s Correlation Coefficient (MCC) of 0.8950. While on the 67,584 dataset, MutTMPredictor achieves an MCC of 0.7523 and area under curve (AUC) of 0.8746, which are 0.1625 and 0.0801 respectively higher than those of the existing best predictor (fathmm). Besides, MutTMPredictor also outperforms two specific predictors on the Pred-MutHTP datasets. The results suggest that MutTMPredictor can be used as an effective method for predicting and prioritizing missense mutations in transmembrane proteins. The MutTMPredictor webserver and datasets are freely accessible at http://csbio.njust.edu.cn/bioinf/muttmpredictor/ for academic use.

10 citations

Book ChapterDOI
18 Oct 2019
TL;DR: A novel deep-learning model, called Combination of Multi-Scale Convolutional Network and Long Short-Term Memory Network (MCNN-LSTM), which utilizes multi-scale convolution for feature processing, and the long short-term memory network to recognize TFBS in DNA sequences is proposed.
Abstract: Transcription factor binding site (TFBS), one of the DNA-protein binding sites, plays important roles in understanding regulation of gene expression and drug design. Recently, deep-learning based methods have been widely used in the prediction of TFBS. In this work, we propose a novel deep-learning model, called Combination of Multi-Scale Convolutional Network and Long Short-Term Memory Network (MCNN-LSTM), which utilizes multi-scale convolution for feature processing, and the long short-term memory network to recognize TFBS in DNA sequences. Moreover, we design a new encoding method, called multi-nucleotide one-hot (MNOH), which considers the correlation between nucleotides in adjacent positions, to further improve the prediction performance of TFBS. Stringent cross-validation and independent tests on benchmark datasets demonstrated the efficacy of MNOH and MCNN-LSTM. Based on the proposed methods, we further implement a new TFBS predictor, called DeepTF. The computational experimental results show that our predictor outperformed several existing TFBS predictors.

9 citations


Cited by
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Journal ArticleDOI
TL;DR: iLearnPlus as discussed by the authors is a machine learning platform with graphical-and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences.
Abstract: Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development of new predictive models, which depends on the availability of effective tools that support these efforts. We introduce iLearnPlus, the first machine-learning platform with graphical- and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences. iLearnPlus provides a comprehensive set of algorithms and automates sequence-based feature extraction and analysis, construction and deployment of models, assessment of predictive performance, statistical analysis, and data visualization; all without programming. iLearnPlus includes a wide range of feature sets which encode information from the input sequences and over twenty machine-learning algorithms that cover several deep-learning approaches, outnumbering the current solutions by a wide margin. Our solution caters to experienced bioinformaticians, given the broad range of options, and biologists with no programming background, given the point-and-click interface and easy-to-follow design process. We showcase iLearnPlus with two case studies concerning prediction of long noncoding RNAs (lncRNAs) from RNA transcripts and prediction of crotonylation sites in protein chains. iLearnPlus is an open-source platform available at https://github.com/Superzchen/iLearnPlus/ with the webserver at http://ilearnplus.erc.monash.edu/.

90 citations

Journal ArticleDOI
TL;DR: This resource provides central access to structures in the Protein Data Bank (PDB), along with functional annotations, associated homology models, worldwide protein target tracking information, available protocols and the potential to obtain DNA materials for many of the targets.
Abstract: The Protein Structure Initiative Structural Genomics Knowledgebase (PSI SGKB, http://kb.psi-structuralgenomics.org) has been created to turn the products of the PSI structural genomics effort into knowledge that can be used by the biological research community to understand living systems and disease. This resource provides central access to structures in the Protein Data Bank (PDB), along with functional annotations, associated homology models, worldwide protein target tracking information, available protocols and the potential to obtain DNA materials for many of the targets. It also offers the ability to search all of the structural and methodological publications and the innovative technologies that were catalyzed by the PSI's high-throughput research efforts. In collaboration with the Nature Publishing Group, the PSI SGKB provides a research library, editorials about new research advances, news and an events calendar to present a broader view of structural biology and structural genomics. By making these resources freely available, the PSI SGKB serves as a bridge to connect the structural biology and the greater biomedical communities.

88 citations

Journal ArticleDOI
TL;DR: Ten different feature encoding schemes were explored, with the goal of capturing key characteristics around 6mA sites and Meta-i6mA was proposed that combined the baseline models using the meta-predictor approach and outperformed the existing predictors.
Abstract: DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one of the challenging tasks in genome analysis, which leads to an understanding of their biological functions. To date, several species-specific machine learning (ML)-based models have been proposed, but majority of them did not test their model to other species. Hence, their practical application to other plant species is quite limited. In this study, we explored 10 different feature encoding schemes, with the goal of capturing key characteristics around 6mA sites. We selected five feature encoding schemes based on physicochemical and position-specific information that possesses high discriminative capability. The resultant feature sets were inputted to six commonly used ML methods (random forest, support vector machine, extremely randomized tree, logistic regression, naive Bayes and AdaBoost). The Rosaceae genome was employed to train the above classifiers, which generated 30 baseline models. To integrate their individual strength, Meta-i6mA was proposed that combined the baseline models using the meta-predictor approach. In extensive independent test, Meta-i6mA showed high Matthews correlation coefficient values of 0.918, 0.827 and 0.635 on Rosaceae, rice and Arabidopsis thaliana, respectively and outperformed the existing predictors. We anticipate that the Meta-i6mA can be applied across different plant species. Furthermore, we developed an online user-friendly web server, which is available at http://kurata14.bio.kyutech.ac.jp/Meta-i6mA/.

81 citations

Journal ArticleDOI
TL;DR: i et al. as mentioned in this paper proposed an integrated prediction tool, iStable 2.0, which integrates 11 sequence-based and structure-based prediction tools by machine learning and adds protein sequence information as features.
Abstract: Protein mutations can lead to structural changes that affect protein function and result in disease occurrence. In protein engineering, drug design or and optimization industries, mutations are often used to improve protein stability or to change protein properties while maintaining stability. To provide possible candidates for novel protein design, several computational tools for predicting protein stability changes have been developed. Although many prediction tools are available, each tool employs different algorithms and features. This can produce conflicting prediction results that make it difficult for users to decide upon the correct protein design. Therefore, this study proposes an integrated prediction tool, iStable 2.0, which integrates 11 sequence-based and structure-based prediction tools by machine learning and adds protein sequence information as features. Three coding modules are designed for the system, an Online Server Module, a Stand-alone Module and a Sequence Coding Module, to improve the prediction performance of the previous version of the system. The final integrated structure-based classification model has a higher Matthews correlation coefficient than that of the single prediction tool (0.708 vs 0.547, respectively), and the Pearson correlation coefficient of the regression model likewise improves from 0.669 to 0.714. The sequence-based model not only successfully integrates off-the-shelf predictors but also improves the Matthews correlation coefficient of the best single prediction tool by at least 0.161, which is better than the individual structure-based prediction tools. In addition, both the Sequence Coding Module and the Stand-alone Module maintain performance with only a 5% decrease of the Matthews correlation coefficient when the integrated online tools are unavailable. iStable 2.0 is available at http://ncblab.nchu.edu.tw/iStable2.

61 citations

Journal ArticleDOI
TL;DR: In this paper, a machine learning-based meta-predictor called NeuroPred-FRL was developed by employing the feature representation learning approach, where the predicted probability scores of NPs based on the 66 baseline models were combined as the input feature vector.
Abstract: Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.

48 citations