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Article: DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis.

Zhang, Di / Bin, Yannan

2021 Volume 11, Page(s) 607798

Abstract: Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. Here, an effective and no parameter algorithm, named DriverSubNet, is presented for detecting driver genes by effectively mining the ... ...

Abstract	Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. Here, an effective and no parameter algorithm, named DriverSubNet, is presented for detecting driver genes by effectively mining the mutation and gene expression information based on subnetwork enrichment analysis. Compared with the existing classic methods, DriverSubNet can rank driver genes and filter out passenger genes more efficiently in terms of precision, recall, and F1 score, as indicated by the analysis of four cancer datasets. The method recovered about 50% more known cancer driver genes in the top 100 detected genes than those found in other algorithms. Intriguingly, DriverSubNet was able to find these unknown cancer driver genes which could act as potential therapeutic targets and useful prognostic biomarkers for cancer patients. Therefore, DriverSubNet may act as a useful tool for the identification of driver genes by subnetwork enrichment analysis.
Language	English
Publishing date	2021-02-19
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2020.607798
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Article ; Online: PACVP: Prediction of Anti-Coronavirus Peptides Using a Stacking Learning Strategy With Effective Feature Representation.

Chen, Shouzhi / Liao, Yanhong / Zhao, Jianping / Bin, Yannan / Zheng, Chunhou

IEEE/ACM transactions on computational biology and bioinformatics

2023 Volume 20, Issue 5, Page(s) 3106–3116

Abstract: Due to the global outbreak of COVID-19 and its variants, antiviral peptides with anti-coronavirus activity (ACVPs) represent a promising new drug candidate for the treatment of coronavirus infection. At present, several computational tools have been ... ...

Abstract	Due to the global outbreak of COVID-19 and its variants, antiviral peptides with anti-coronavirus activity (ACVPs) represent a promising new drug candidate for the treatment of coronavirus infection. At present, several computational tools have been developed to identify ACVPs, but the overall prediction performance is still not enough to meet the actual therapeutic application. In this study, we constructed an efficient and reliable prediction model PACVP (Prediction of Anti-CoronaVirus Peptides) for identifying ACVPs based on effective feature representation and a two-layer stacking learning framework. In the first layer, we use nine feature encoding methods with different feature representation angles to characterize the rich sequence information and fuse them into a feature matrix. Secondly, data normalization and unbalanced data processing are carried out. Next, 12 baseline models are constructed by combining three feature selection methods and four machine learning classification algorithms. In the second layer, we input the optimal probability features into the logistic regression algorithm (LR) to train the final model PACVP. The experiments show that PACVP achieves favorable prediction performance on independent test dataset, with ACC of 0.9208 and AUC of 0.9465. We hope that PACVP will become a useful method for identifying, annotating and characterizing novel ACVPs.
MeSH term(s)	Humans ; Peptides ; Algorithms ; COVID-19 ; Machine Learning ; Probability
Chemical Substances	Peptides
Language	English
Publishing date	2023-10-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2023.3238370
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Article ; Online: PrMFTP: Multi-functional therapeutic peptides prediction based on multi-head self-attention mechanism and class weight optimization.

Yan, Wenhui / Tang, Wending / Wang, Lihua / Bin, Yannan / Xia, Junfeng

PLoS computational biology

2022 Volume 18, Issue 9, Page(s) e1010511

Abstract: Prediction of therapeutic peptide is a significant step for the discovery of promising therapeutic drugs. Most of the existing studies have focused on the mono-functional therapeutic peptide prediction. However, the number of multi-functional therapeutic ...

Abstract	Prediction of therapeutic peptide is a significant step for the discovery of promising therapeutic drugs. Most of the existing studies have focused on the mono-functional therapeutic peptide prediction. However, the number of multi-functional therapeutic peptides (MFTP) is growing rapidly, which requires new computational schemes to be proposed to facilitate MFTP discovery. In this study, based on multi-head self-attention mechanism and class weight optimization algorithm, we propose a novel model called PrMFTP for MFTP prediction. PrMFTP exploits multi-scale convolutional neural network, bi-directional long short-term memory, and multi-head self-attention mechanisms to fully extract and learn informative features of peptide sequence to predict MFTP. In addition, we design a class weight optimization scheme to address the problem of label imbalanced data. Comprehensive evaluation demonstrate that PrMFTP is superior to other state-of-the-art computational methods for predicting MFTP. We provide a user-friendly web server of PrMFTP, which is available at http://bioinfo.ahu.edu.cn/PrMFTP.
MeSH term(s)	Algorithms ; Peptides/therapeutic use
Chemical Substances	Peptides
Language	English
Publishing date	2022-09-12
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1010511
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Article ; Online: FFMAVP: a new classifier based on feature fusion and multitask learning for identifying antiviral peptides and their subclasses.

Cao, Ruifen / Hu, Weiling / Wei, Pijing / Ding, Yun / Bin, Yannan / Zheng, Chunhou

Briefings in bioinformatics

2023 Volume 24, Issue 6

Abstract: Antiviral peptides (AVPs) are widely found in animals and plants, with high specificity and strong sensitivity to drug-resistant viruses. However, due to the great heterogeneity of different viruses, most of the AVPs have specific antiviral activities. ... ...

Abstract	Antiviral peptides (AVPs) are widely found in animals and plants, with high specificity and strong sensitivity to drug-resistant viruses. However, due to the great heterogeneity of different viruses, most of the AVPs have specific antiviral activities. Therefore, it is necessary to identify the specific activities of AVPs on virus types. Most existing studies only identify AVPs, with only a few studies identifying subclasses by training multiple binary classifiers. We develop a two-stage prediction tool named FFMAVP that can simultaneously predict AVPs and their subclasses. In the first stage, we identify whether a peptide is AVP or not. In the second stage, we predict the six virus families and eight species specifically targeted by AVPs based on two multiclass tasks. Specifically, the feature extraction module in the two-stage task of FFMAVP adopts the same neural network structure, in which one branch extracts features based on amino acid feature descriptors and the other branch extracts sequence features. Then, the two types of features are fused for the following task. Considering the correlation between the two tasks of the second stage, a multitask learning model is constructed to improve the effectiveness of the two multiclass tasks. In addition, to improve the effectiveness of the second stage, the network parameters trained through the first-stage data are used to initialize the network parameters in the second stage. As a demonstration, the cross-validation results, independent test results and visualization results show that FFMAVP achieves great advantages in both stages.
MeSH term(s)	Algorithms ; Peptides/chemistry ; Neural Networks, Computer ; Machine Learning ; Antiviral Agents/pharmacology ; Antiviral Agents/chemistry
Chemical Substances	Peptides ; Antiviral Agents
Language	English
Publishing date	2023-10-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbad353
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Article ; Online: NeuroPred-CLQ: incorporating deep temporal convolutional networks and multi-head attention mechanism to predict neuropeptides.

Chen, Shouzhi / Li, Qing / Zhao, Jianping / Bin, Yannan / Zheng, Chunhou

Briefings in bioinformatics

2022 Volume 23, Issue 5

Abstract: Neuropeptides (NPs) are a particular class of informative substances in the immune system and physiological regulation. They play a crucial role in regulating physiological functions in various biological growth and developmental stages. In addition, NPs ...

Abstract	Neuropeptides (NPs) are a particular class of informative substances in the immune system and physiological regulation. They play a crucial role in regulating physiological functions in various biological growth and developmental stages. In addition, NPs are crucial for developing new drugs for the treatment of neurological diseases. With the development of molecular biology techniques, some data-driven tools have emerged to predict NPs. However, it is necessary to improve the predictive performance of these tools for NPs. In this study, we developed a deep learning model (NeuroPred-CLQ) based on the temporal convolutional network (TCN) and multi-head attention mechanism to identify NPs effectively and translate the internal relationships of peptide sequences into numerical features by the Word2vec algorithm. The experimental results show that NeuroPred-CLQ learns data information effectively, achieving 93.6% accuracy and 98.8% AUC on the independent test set. The model has better performance in identifying NPs than the state-of-the-art predictors. Visualization of features using t-distribution random neighbor embedding shows that the NeuroPred-CLQ can clearly distinguish the positive NPs from the negative ones. We believe the NeuroPred-CLQ can facilitate drug development and clinical trial studies to treat neurological disorders.
MeSH term(s)	Algorithms ; Neuropeptides/genetics ; Peptides/chemistry
Chemical Substances	Neuropeptides ; Peptides
Language	English
Publishing date	2022-08-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbac319
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function.

Fan, Henghui / Yan, Wenhui / Wang, Lihua / Liu, Jie / Bin, Yannan / Xia, Junfeng

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 6

Abstract: Motivation: With the great number of peptide sequences produced in the postgenomic era, it is highly desirable to identify the various functions of therapeutic peptides quickly. Furthermore, it is a great challenge to predict accurate multi-functional ... ...

Abstract	Motivation: With the great number of peptide sequences produced in the postgenomic era, it is highly desirable to identify the various functions of therapeutic peptides quickly. Furthermore, it is a great challenge to predict accurate multi-functional therapeutic peptides (MFTP) via sequence-based computational tools. Results: Here, we propose a novel multi-label-based method, named ETFC, to predict 21 categories of therapeutic peptides. The method utilizes a deep learning-based model architecture, which consists of four blocks: embedding, text convolutional neural network, feed-forward network, and classification blocks. This method also adopts an imbalanced learning strategy with a novel multi-label focal dice loss function. multi-label focal dice loss is applied in the ETFC method to solve the inherent imbalance problem in the multi-label dataset and achieve competitive performance. The experimental results state that the ETFC method is significantly better than the existing methods for MFTP prediction. With the established framework, we use the teacher-student-based knowledge distillation to obtain the attention weight from the self-attention mechanism in the MFTP prediction and quantify their contributions toward each of the investigated activities. Availability and implementation: The source code and dataset are available via: https://github.com/xialab-ahu/ETFC.
MeSH term(s)	Humans ; Deep Learning ; Neural Networks, Computer ; Peptides/therapeutic use ; Software
Chemical Substances	Peptides
Language	English
Publishing date	2023-05-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad334
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Article ; Online: UsIL-6: An unbalanced learning strategy for identifying IL-6 inducing peptides by undersampling technique.

Liao, Yan-Hong / Chen, Shou-Zhi / Bin, Yan-Nan / Zhao, Jian-Ping / Feng, Xin-Long / Zheng, Chun-Hou

Computer methods and programs in biomedicine

2024 Volume 250, Page(s) 108176

Abstract: Background and objective: Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the ... ...

Abstract	Background and objective: Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the development of diagnosis and immunotherapy. Although the existing methods have some success in predicting IL-6 inducing peptides, there is still room for improvement in the performance of these models in practical application. Methods: In this study, we proposed UsIL-6, a high-performance bioinformatics tool for identifying IL-6 inducing peptides. First, we extracted five groups of physicochemical properties and sequence structural information from IL-6 inducing peptide sequences, and obtained a 636-dimensional feature vector, we also employed NearMiss3 undersampling method and normalization method StandardScaler to process the data. Then, a 40-dimensional optimal feature vector was obtained by Boruta feature selection method. Finally, we combined this feature vector with extreme randomization tree classifier to build the final model UsIL-6. Results: The AUC value of UsIL-6 on the independent test dataset was 0.87, and the BACC value was 0.808, which indicated that UsIL-6 had better performance than the existing methods in IL-6 inducing peptide recognition. Conclusions: The performance comparison on independent test dataset confirmed that UsIL-6 could achieve the highest performance, best robustness, and most excellent generalization ability. We hope that UsIL-6 will become a valuable method to identify, annotate and characterize new IL-6 inducing peptides.
Language	English
Publishing date	2024-04-12
Publishing country	Ireland
Document type	Journal Article
ZDB-ID	632564-6
ISSN	1872-7565 ; 0169-2607
ISSN (online)	1872-7565
ISSN	0169-2607
DOI	10.1016/j.cmpb.2024.108176
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Article: DLFF-ACP: prediction of ACPs based on deep learning and multi-view features fusion.

Cao, Ruifen / Wang, Meng / Bin, Yannan / Zheng, Chunhou

PeerJ

2021 Volume 9, Page(s) e11906

Abstract: An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, ...

Abstract	An emerging type of therapeutic agent, anticancer peptides (ACPs), has attracted attention because of its lower risk of toxic side effects. However process of identifying ACPs using experimental methods is both time-consuming and laborious. In this study, we developed a new and efficient algorithm that predicts ACPs by fusing multi-view features based on dual-channel deep neural network ensemble model. In the model, one channel used the convolutional neural network CNN to automatically extract the potential spatial features of a sequence. Another channel was used to process and extract more effective features from handcrafted features. Additionally, an effective feature fusion method was explored for the mutual fusion of different features. Finally, we adopted the neural network to predict ACPs based on the fusion features. The performance comparisons across the single and fusion features showed that the fusion of multi-view features could effectively improve the model's predictive ability. Among these, the fusion of the features extracted by the CNN and composition of k-spaced amino acid group pairs achieved the best performance. To further validate the performance of our model, we compared it with other existing methods using two independent test sets. The results showed that our model's area under curve was 0.90, which was higher than that of the other existing methods on the first test set and higher than most of the other existing methods on the second test set. The source code and datasets are available at https://github.com/wame-ng/DLFF-ACP.
Language	English
Publishing date	2021-08-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	2703241-3
ISSN	2167-8359
ISSN	2167-8359
DOI	10.7717/peerj.11906
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Article ; Online: Prediction of circRNA-disease associations based on inductive matrix completion.

Li, Menglu / Liu, Mengya / Bin, Yannan / Xia, Junfeng

BMC medical genomics

2020 Volume 13, Issue Suppl 5, Page(s) 42

Abstract: Background: Currently, numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. While identifying disease-related circRNAs in vivo is time- and labor-consuming, a feasible and effective computational ... ...

Abstract	Background: Currently, numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. While identifying disease-related circRNAs in vivo is time- and labor-consuming, a feasible and effective computational method to predict circRNA-disease associations is worthy of more studies. Results: Here, we present a new method called SIMCCDA (Speedup Inductive Matrix Completion for CircRNA-Disease Associations prediction) to predict circRNA-disease associations. Based on known circRNA-disease associations, circRNA sequence similarity, disease semantic similarity, and the computed Gaussian interaction profile kernel similarity, we used speedup inductive matrix completion to construct the model. The proposed SIMCCDA method obtains an area under ROC curve (AUC) of 0.8465 with leave-one-out cross validation in the dataset, which is obtained by the combination of the three databases (circRNA disease, circ2Disease and circR2Disease). Our method surpasses other state-of-art models in predicting circRNA-disease associations. Furthermore, we conducted case studies in breast cancer, stomach cancer and colorectal cancer for further performance evaluation. Conclusion: All the results show reliable prediction ability of SIMCCDA. We anticipate that SIMCCDA could be utilized to facilitate further developments in the field and follow-up investigations by biomedical researchers.
MeSH term(s)	Algorithms ; Biomarkers, Tumor/genetics ; Computational Biology/methods ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Humans ; Neoplasms/genetics ; Neoplasms/pathology ; Prognosis ; RNA, Circular/genetics ; RNA-Seq/methods ; Transcriptome
Chemical Substances	Biomarkers, Tumor ; RNA, Circular
Language	English
Publishing date	2020-04-03
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	1755-8794
ISSN (online)	1755-8794
DOI	10.1186/s12920-020-0679-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Predicting Hot Spot Residues at Protein-DNA Binding Interfaces Based on Sequence Information.

Yao, Lingsong / Wang, Huadong / Bin, Yannan

Interdisciplinary sciences, computational life sciences

2020 Volume 13, Issue 1, Page(s) 1–11

Abstract: Hot spot residues at protein-DNA binding interfaces are hugely important for investigating the underlying mechanism of molecular recognition. Currently, there are a few tools available for identifying the hot spot residues in the protein-DNA complexes. ... ...

Abstract	Hot spot residues at protein-DNA binding interfaces are hugely important for investigating the underlying mechanism of molecular recognition. Currently, there are a few tools available for identifying the hot spot residues in the protein-DNA complexes. In addition, the three-dimensional protein structures are needed in these tools. However, it is well known that the three-dimensional structures are unavailable for most proteins. Considering the limitation, we proposed a method, named SPDH, for predicting hot spot residues only based on protein sequences. Firstly, we obtained 133 features from physicochemical property, conservation, predicted solvent accessible surface area and structure. Then, we systematically assessed these features based on various feature selection methods to obtain the optimal feature subset and compared the models using four classical machine learning algorithms (support vector machine, random forest, logistic regression, and k-nearest neighbor) on the training dataset. We found that the variability of physicochemical property features between wild and mutative types was important on improving the performance of the prediction model. On the independent test set, our method achieved the performance with AUC of 0.760 and sensitivity of 0.808, and outperformed other methods. The data and source code can be downloaded at https://github.com/xialab-ahu/SPDH .
MeSH term(s)	Algorithms ; Computational Biology ; DNA ; Databases, Protein ; Protein Binding ; Proteins/metabolism
Chemical Substances	Proteins ; DNA (9007-49-2)
Language	English
Publishing date	2020-10-17
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2493085-4
ISSN	1867-1462 ; 1913-2751
ISSN (online)	1867-1462
ISSN	1913-2751
DOI	10.1007/s12539-020-00399-z
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