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Drug Repurposing for Newly Emerged Diseases via network-based inference on a Gene-disease-drug network

MOLECULAR INFORMATICS 2022年第9期41卷 2200001-2200001页

作者： Qin, Li Wang, Jiye Wu, Zengrui Li, Weihua Liu, Guixia Tang, Yun East China Univ Sci & Technol Sch Pharm Shanghai Frontiers Sci Ctr Optogenet Tech Cell Me Shanghai 200237 Peoples R China

Identification of disease-drug associations is an effective strategy for drug repurposing, especially in searching old drugs for newly emerged diseases like COVID-19. In this study, we put forward a network-based method named NEDNBI to predict disease-drug associations based on a gene-disease-drug tripartite network, which could be applied in drug repurposing. The novelty of our method lies in the fact that no negative data are required, and new disease could be added into the disease-drug network with gene as the bridge. The comprehensive evaluation results showed that the proposed method had good performance, with AUC value 0.948 +/- 0.009 for 10-fold cross validation. In a case study, 8 of the 20 predicted old drugs have been tested clinically for the treatment of COVID-19, which illustrated the usefulness of our method in drug repurposing. The source code and data of the method are available at .

关键词： Drug repurposing Disease-drug associations network-based inference

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mtADENet: A novel interpretable method integrating multiple types of network-based inference approaches for prediction of adverse drug events

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COMPUTERS IN BIOLOGY AND MEDICINE 2024年 168卷 107831-107831页

作者： Yu, Zhuohang Wu, Zengrui Zhou, Moran Chen, Long Li, Weihua Liu, Guixia Tang, Yun East China Univ Sci & Technol Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met Sch Pharm Shanghai Key Lab New Drug Design 130 Meilong Rd Shanghai 200237 Peoples R China

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and accelerate drug safety assessment. ADEs are mainly caused by unintended interactions with primary or additional targets (off-targets). In this study, we proposed a novel interpretable method named mtADENet, which integrates multiple types of network-based inference approaches for ADE prediction. Different from phenotype-based methods, mtADENet introduced computational target profiles predicted by network-based methods to bridge the gap between chemical structures and ADEs, and hence can not only predict ADEs for drugs and novel compounds within or outside the drug-ADE association network, but also provide insights for the elucidation of molecular mechanisms of the ADEs caused by drugs. We constructed a series of network-based prediction models for 23 ADE categories. These models achieved high AUC values ranging from 0.865 to 0.942 in 10-fold cross validation. The best model further showed high performance on four external validation sets, which outperformed two previous network-based methods. To show the practical value of mtADENet, we performed case studies on developmental neurotoxicity and cardio-oncology, and over 50 % of predicted ADEs and targets for drugs and novel compounds were validated by literature. Moreover, mtADENet is freely available at our web server named NetInfer (http:// ***/netinfer/). In summary, mtADENet would be a powerful tool for ADE prediction and drug safety assessment in drug discovery and development.

关键词： Adverse drug event network-based inference Target profile Computational prediction Molecular mechanism

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Drug repositioning by prediction of drug's anatomical therapeutic chemical code via network-based inference approaches

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BRIEFINGS IN BIOINFORMATICS 2021年第2期22卷 2058-2072页

作者： Peng, Yayuan Wang, Manjiong Xu, Yixiang Wu, Zengrui Wang, Jiye Zhang, Chao Liu, Guixia Li, Weihua Li, Jian Tang, Yun East China Univ Sci & Technol Shanghai Peoples R China

Drug discovery and development is a time-consuming and costly process. Therefore, drug repositioning has become an effective approach to address the issues by identifying new therapeutic or pharmacological actions for existing drugs. The drug's anatomical therapeutic chemical (ATC) code is a hierarchical classification system categorized as five levels according to the organs or systems that drugs act and the pharmacology, therapeutic and chemical properties of drugs. The 2nd-, 3rd-and 4th-level ATC codes reserved the therapeutic and pharmacological information of drugs. With the hypothesis that drugs with similar structures or targets would possess similar ATC codes, we exploited a network-based approach to predict the 2nd-, 3rd- and 4th-level ATC codes by constructing substructure drug-ATC (SD-ATC), target drug-ATC (TD-ATC) and Substructure&Target drug-ATC (STD-ATC) networks. After 10-fold cross validation and two external validations, the STD-ATC models outperformed the SD-ATC and TD-ATC ones. Furthermore, with KR as fingerprint, the STD-ATC model was identified as the optimal model with AUC values at 0.899 +/- 0.015, 0.916 and 0.893 for 10-fold cross validation, external validation set 1 and external validation set 2, respectively. To illustrate the predictive capability of the STD-ATC model with KR fingerprint, as a case study, we predicted 25 FDA-approved drugs (22 drugs were actually purchased) to have potential activities on heart failure using that model. Experiments in vitro confirmed that 8 of the 22 old drugs have shown mild to potent cardioprotective activities on both hypoxia model and oxygen-glucose deprivation model, which demonstrated that our STD-ATC prediction model would be an effective tool for drug repositioning.

关键词： anatomical therapeutic chemical code drug repositioning network-based inference

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NBPMF: Novel network-based inference Methods for Peptide Mass Fingerprinting 16

NBPMF: Novel Network-Based Inference Methods for Peptide Mas...

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IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Liang, Zhewei Lajoie, Gilles Zhang, Kaizhong Nivers Western Ontario Epartment Comp Sci Ondon ON NA 5B Canada Nivers Western Ontario Epartment Biochem Ondon ON NA 5B8 Canada

ISBN: (纸本)9781538607718

Mass spectrometry (MS) has recently become a primary tool for protein identification and quantification. Peptide mass fingerprinting (PMF) is widely used to identify proteins from MS data. Conventional PMF representatives such as probabilistic MOWSE algorithm, is based on mass distribution of tryptic peptides. In this paper we develop a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, we developed new peptide protein matching score functions. We present two methods: the static one, Pr S, is based on an independent probability framework;and the dynamic one, H atS, depicts input data from dependent perspective. We also use linear regression to adjust the matching score according to the masses of proteins. In addition, we consider the order of retention time to further correct the score function. In the post processing, we restrict that a peak can only be assigned to one peptide in order to reduce random matches. Finally, we try to filter false positive proteins for better result. The experiments on simulated and real data demonstrate that our NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.

关键词： network-based inference peptide mass fingerprinting proteomics protein identification mass spectrometry computational intelligence

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NBPMF: Novel network-based inference Methods for Peptide Mass Fingerprinting

NBPMF: Novel Network-Based Inference Methods for Peptide Mas...

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作者： Zhewei Liang The University of Western Ontario

学位级别：博士

Proteins are large, complex molecules that perform a vast array of functions in every liv- ing cell. A proteome is a set of proteins produced in an organism, and proteomics is the large-scale study of proteomes. Several high-throughput technologies have been developed in proteomics, where the most commonly applied are mass spectrometry (MS) based approaches. MS is an analytical technique for determining the composition of a sample. Recently it has become a primary tool for protein identification, quantification, and post translational modifi- cation (PTM) characterization in proteomics research. There are usually two different ways to identify proteins: top-down and bottom-up. Top-down approaches are based on subjecting in- tact protein ions and large fragment ions to tandem MS directly, while bottom-up methods are based on mass spectrometric analysis of peptides derived from proteolytic digestion, usually with trypsin. In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. Conventional PMF representatives such as probabilistic MOWSE algorithm, is based on mass distribution of tryptic peptides. In this thesis, we developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite net- work, we designed new peptide protein matching score functions. We present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input dataset as dependent peptides. Moreover, we use linear regression to ad- just the matching score according to the masses of proteins. In addition, we consider the order of retention time to further correct the score function. In the post processing, we design two algorithms: assignment of peaks, and protein filtration. The former restricts that a peak can only be assigned to one peptide in order to reduce random matches; and the latter assumes each peak can only be assigned to one protein. In th

关键词： network-based inference Peptide Mass Fingerprinting Proteomics Protein Identification Mass Spectrometry Knowledge-Discovery in Databases Recommender Systems Bioinformatics Data Mining

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ADENet: a novel network-based inference method for prediction of drug adverse events

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BRIEFINGS IN BIOINFORMATICS 2022年第2期23卷 bbab580-bbab580页

作者： Yu, Zhuohang Wu, Zengrui Li, Weihua Liu, Guixia Tang, Yun East China Univ Sci & Technol Sch Pharm Shanghai Peoples R China

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and improve drug safety assessment. With an increasing number of biological and medical data, computational methods such as network-based methods were proposed for ADE prediction with high efficiency and low cost. However, previous network-based methods rely on the topological information of known drug-ADE networks, and hence cannot make predictions for novel compounds without any known ADE. In this study, we introduced chemical substructures to bridge the gap between the drug-ADE network and novel compounds, and developed a novel network-based method named ADENet, which can predict potential ADEs for not only drugs within the drug-ADE network, but also novel compounds outside the network. To show the performance of ADENet, we collected drug-ADE associations from a comprehensive database named MetaADEDB and constructed a series of network-based prediction models. These models obtained high area under the receiver operating characteristic curve values ranging from 0.871 to 0.947 in 10-fold cross-validation. The best model further showed high performance in external validation, which outperformed a previous network-based and a recent deep learning-based method. Using several approved drugs as case studies, we found that 32-54% of the predicted ADEs can be validated by the literature, indicating the practical value of ADENet. Moreover, ADENet is freely available at our web server named NetInfer (http://***/netinfer). In summary, our method would provide a promising tool for ADE prediction and drug safety assessment in drug discovery and development.

关键词： adverse drug event network-based inference chemical substructure computational prediction

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A Hybrid Collaborative Filtering Model: RSVD Meets Weighted-network based inference

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JOURNAL OF INTERNET TECHNOLOGY 2016年第6期17卷 1221-1233页

作者： Chen, Jiemin Li, Jianguo Xiao, Jing Tang, Yong Fu, Hailin South China Normal Univ Sch Comp Sci Guangzhou Guangdong Peoples R China

In view of the exponential growth of information, a personalized recommendation has been a critical approach to solving the information overload problem recently. As one of the widest applied recommendation methods, Regularized Singular Value Decomposition (RSVD) conveniently fits the user-item rating matrix by low-rank approximation from explicit user feedback. However, implicit information is also very effective in improving recommendation algorithms, such as the degree correlation of the user-item bipartite network. Consequently, in this paper, we propose a hybrid collaborative filtering model named RSVD_WNBI. It builds on the algorithm RSVD which involves the explicit influence of ratings, and further integrates implicit influence of the degree correlation in the user-item bipartite network from Weighted network-based inference (WNBI) algorithm. Experimental results on three real-world datasets show that our algorithm can yield better performance over already widely used methods in the accuracy of recommendation, especially when few user ratings are observed.

关键词： Personalized recommendation Collaborative filtering Regularized singular value decomposition network-based inference

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A Hybrid Collaborative Filtering Model: RSVD Meets Weighted-network based inference

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网际网路技术学刊 2016年第6期17卷 1221-1233页

作者： Jiemin Chen Jianguo Li Jing Xiao Yong Tang Hailin Fu

关键词： Personalized recommendation Collaborative filtering Regularized singular value decomposition network-based inference

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NBPMF: Novel Peptide Mass Fingerprinting based on network inference

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2017年第4期11卷 41-65页

作者： Liang, Zhewei Lajoie, Gilles Zhang, Kaizhong Univ Western Ontario Dept Comp Sci London ON Canada Univ Western Ontario Dept Biochem London ON Canada

Mass spectrometry (MS) is an analytical technique for determining the composition of a sample. In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. In this article, the authors developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, they designed new peptide protein matching score functions. They present two methods: the static one, ProbS, is based on an independent probability framework;and the dynamic one, HeatS, depicts input data as dependent peptides. The authors also use linear regression to adjust the matching score according to the masses of proteins. In addition, they consider the order of retention time to further correct the score function. In post processing, a peak can only be assigned to one peptide in order to reduce random matches. Finally, the authors try to filter out false positive proteins. The experiments on simulated and real data demonstrate that their NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.

关键词： Bioinformatics Data Mining Knowledge-Discovery in Databases Mass Spectrometry network-based inference Peptide Mass Fingerprinting Protein Identification Proteomics Recommender Systems

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Detecting Phishing URLs with GNN-based network inference

Detecting Phishing URLs with GNN-based Network Inference

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International Conference on networking and network Applications (NaNA)

作者： Wang, Changan Liu, Zhihong Zeng, Yong Xidian Univ Sch Cyber Engn Xian Peoples R China

ISBN: (纸本)9798350376784;9798350376777

Phishing attacks are a widespread form of online fraud with the aim of deceiving users to obtain sensitive information. There are many methods for detecting phishing URLs (Uniform Resource Locators), but most of them require a lot of additional operations, which may incur significant overhead. In this paper, a new graph based reasoning method has been designed, which only utilizes the features extracted from URLs strings to distinguish between phishing and benign URLs. To test the performance of the method, a new dataset of phishing URLs with various evasion techniques is constructed, and we have conducted detection experiments using this dataset. Experimental results demonstrate that the ideas and methods presented in the paper will be useful in the analysis of phishing attacks. It can easily and quickly detect the vast majority of phishing attacks, providing early warning for network users.

关键词： Phishing Detection network Security network-based inference Graph Neural network

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