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Article ; Online: Prediction of Proteins in Cerebrospinal Fluid and Application to Glioma Biomarker Identification.

He, Kai / Wang, Yan / Xie, Xuping / Shao, Dan

2023 Volume 28, Issue 8

Abstract: Cerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF proteins have been identified with wet experiments, the identification of CSF proteins is still a challenge. ...

Abstract	Cerebrospinal fluid (CSF) proteins are very important because they can serve as biomarkers for central nervous system diseases. Although many CSF proteins have been identified with wet experiments, the identification of CSF proteins is still a challenge. In this paper, we propose a novel method to predict proteins in CSF based on protein features. A two-stage feature-selection method is employed to remove irrelevant features and redundant features. The deep neural network and bagging method are used to construct the model for the prediction of CSF proteins. The experiment results on the independent testing dataset demonstrate that our method performs better than other methods in the prediction of CSF proteins. Furthermore, our method is also applied to the identification of glioma biomarkers. A differentially expressed gene analysis is performed on the glioma data. After combining the analysis results with the prediction results of our model, the biomarkers of glioma are identified successfully.
MeSH term(s)	Humans ; Biomarkers/cerebrospinal fluid ; Glioma/diagnosis ; Glioma/genetics ; Cerebrospinal Fluid Proteins ; Central Nervous System Diseases
Chemical Substances	Biomarkers ; Cerebrospinal Fluid Proteins
Language	English
Publishing date	2023-04-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	1413402-0
ISSN	1420-3049 ; 1431-5165 ; 1420-3049
ISSN (online)	1420-3049
ISSN	1431-5165 ; 1420-3049
DOI	10.3390/molecules28083617
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The multiple roles of nsp6 in the molecular pathogenesis of SARS-CoV-2.

Bills, Cody / Xie, Xuping / Shi, Pei-Yong

Antiviral research

2023 Volume 213, Page(s) 105590

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve and adapt after its emergence in late 2019. As the causative agent of the coronavirus disease 2019 (COVID-19), the replication and pathogenesis of SARS-CoV-2 have been ... ...

Abstract	Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve and adapt after its emergence in late 2019. As the causative agent of the coronavirus disease 2019 (COVID-19), the replication and pathogenesis of SARS-CoV-2 have been extensively studied by the research community for vaccine and therapeutics development. Given the importance of viral spike protein in viral infection/transmission and vaccine development, the scientific community has thus far primarily focused on studying the structure, function, and evolution of the spike protein. Other viral proteins are understudied. To fill in this knowledge gap, a few recent studies have identified nonstructural protein 6 (nsp6) as a major contributor to SARS-CoV-2 replication through the formation of replication organelles, antagonism of interferon type I (IFN-I) responses, and NLRP3 inflammasome activation (a major factor of severe disease in COVID-19 patients). Here, we review the most recent progress on the multiple roles of nsp6 in modulating SARS-CoV-2 replication and pathogenesis.
MeSH term(s)	Humans ; SARS-CoV-2 ; COVID-19 ; Spike Glycoprotein, Coronavirus/chemistry ; Viral Proteins ; Interferon Type I
Chemical Substances	Spike Glycoprotein, Coronavirus ; Viral Proteins ; Interferon Type I ; spike protein, SARS-CoV-2
Language	English
Publishing date	2023-03-31
Publishing country	Netherlands
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	306628-9
ISSN	1872-9096 ; 0166-3542
ISSN (online)	1872-9096
ISSN	0166-3542
DOI	10.1016/j.antiviral.2023.105590
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Predicting miRNA-disease associations based on PPMI and attention network

Xuping Xie / Yan Wang / Kai He / Nan Sheng

BMC Bioinformatics, Vol 24, Iss 1, Pp 1-

2023 Volume 19

Abstract: Abstract Background With the development of biotechnology and the accumulation of theories, many studies have found that microRNAs (miRNAs) play an important role in various diseases. Uncovering the potential associations between miRNAs and diseases is ... ...

Abstract	Abstract Background With the development of biotechnology and the accumulation of theories, many studies have found that microRNAs (miRNAs) play an important role in various diseases. Uncovering the potential associations between miRNAs and diseases is helpful to better understand the pathogenesis of complex diseases. However, traditional biological experiments are expensive and time-consuming. Therefore, it is necessary to develop more efficient computational methods for exploring underlying disease-related miRNAs. Results In this paper, we present a new computational method based on positive point-wise mutual information (PPMI) and attention network to predict miRNA-disease associations (MDAs), called PATMDA. Firstly, we construct the heterogeneous MDA network and multiple similarity networks of miRNAs and diseases. Secondly, we respectively perform random walk with restart and PPMI on different similarity network views to get multi-order proximity features and then obtain high-order proximity representations of miRNAs and diseases by applying the convolutional neural network to fuse the learned proximity features. Then, we design an attention network with neural aggregation to integrate the representations of a node and its heterogeneous neighbor nodes according to the MDA network. Finally, an inner product decoder is adopted to calculate the relationship scores between miRNAs and diseases. Conclusions PATMDA achieves superior performance over the six state-of-the-art methods with the area under the receiver operating characteristic curve of 0.933 and 0.946 on the HMDD v2.0 and HMDD v3.2 datasets, respectively. The case studies further demonstrate the validity of PATMDA for discovering novel disease-associated miRNAs.
Keywords	MiRNA-disease association prediction ; PPMI ; Attention network ; Deep learning ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Predicting miRNA-disease associations based on PPMI and attention network.

Xie, Xuping / Wang, Yan / He, Kai / Sheng, Nan

BMC bioinformatics

2023 Volume 24, Issue 1, Page(s) 113

Abstract: Background: With the development of biotechnology and the accumulation of theories, many studies have found that microRNAs (miRNAs) play an important role in various diseases. Uncovering the potential associations between miRNAs and diseases is helpful ... ...

Abstract	Background: With the development of biotechnology and the accumulation of theories, many studies have found that microRNAs (miRNAs) play an important role in various diseases. Uncovering the potential associations between miRNAs and diseases is helpful to better understand the pathogenesis of complex diseases. However, traditional biological experiments are expensive and time-consuming. Therefore, it is necessary to develop more efficient computational methods for exploring underlying disease-related miRNAs. Results: In this paper, we present a new computational method based on positive point-wise mutual information (PPMI) and attention network to predict miRNA-disease associations (MDAs), called PATMDA. Firstly, we construct the heterogeneous MDA network and multiple similarity networks of miRNAs and diseases. Secondly, we respectively perform random walk with restart and PPMI on different similarity network views to get multi-order proximity features and then obtain high-order proximity representations of miRNAs and diseases by applying the convolutional neural network to fuse the learned proximity features. Then, we design an attention network with neural aggregation to integrate the representations of a node and its heterogeneous neighbor nodes according to the MDA network. Finally, an inner product decoder is adopted to calculate the relationship scores between miRNAs and diseases. Conclusions: PATMDA achieves superior performance over the six state-of-the-art methods with the area under the receiver operating characteristic curve of 0.933 and 0.946 on the HMDD v2.0 and HMDD v3.2 datasets, respectively. The case studies further demonstrate the validity of PATMDA for discovering novel disease-associated miRNAs.
MeSH term(s)	Humans ; MicroRNAs/genetics ; Algorithms ; Neural Networks, Computer ; ROC Curve ; Gene Regulatory Networks ; Computational Biology/methods ; Genetic Predisposition to Disease
Chemical Substances	MicroRNAs
Language	English
Publishing date	2023-03-23
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-023-05152-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Ru doped NiMoO

Wang, Guoguo / Chen, Qiuyue / Zhang, Jing / An, Xuguan / Liu, Qian / Xie, Lisi / Yao, Weitang / Sun, Xuping / Kong, Qingquan

Journal of colloid and interface science

2024 Volume 661, Page(s) 401–408

Abstract: The electrocatalytic reduction of nitrite to recyclable ammonia ( ... ...

Abstract	The electrocatalytic reduction of nitrite to recyclable ammonia (NH
Language	English
Publishing date	2024-01-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	241597-5
ISSN	1095-7103 ; 0021-9797
ISSN (online)	1095-7103
ISSN	0021-9797
DOI	10.1016/j.jcis.2024.01.195
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Survey of Deep Learning for Detecting miRNA-Disease Associations: Databases, Computational Methods, Challenges, and Future Directions.

Sheng, Nan / Xie, Xuping / Wang, Yan / Huang, Lan / Zhang, Shuangquan / Gao, Ling

IEEE/ACM transactions on computational biology and bioinformatics

2024 Volume PP

Abstract: MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease ... ...

Abstract	MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.
Language	English
Publishing date	2024-01-09
Publishing country	United States
Document type	Journal Article
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2024.3351752
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Making sense of spike D614G in SARS-CoV-2 transmission.

Shi, Aria C / Xie, Xuping

Science China. Life sciences

2021 Volume 64, Issue 7, Page(s) 1062–1067

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of the current coronavirus disease 2019 (COVID-19) pandemic, has evolved to adapt to human host and transmission over the past 12 months. One prominent adaptive mutation is ...

Abstract	Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of the current coronavirus disease 2019 (COVID-19) pandemic, has evolved to adapt to human host and transmission over the past 12 months. One prominent adaptive mutation is the asparagine-to-glycine substitution at amino acid position 614 in the viral spike protein (D614G), which has become dominant in the currently circulating virus strains. Since spike protein determines host ranges, tissue tropism, and pathogenesis through binding to the cellular receptor of angiotensin converting enzyme 2 (ACE2), the D614G mutation is hypothesized to enhance viral fitness in human host, leading to increased transmission during the global pandemic. Here we summarize the recent progress on the role of the D614G mutation in viral replication, pathogenesis, transmission, and vaccine and therapeutic antibody development. These findings underscore the importance in closely monitoring viral evolution and defining their functions to ensure countermeasure efficacy against newly emerging variants.
MeSH term(s)	Angiotensin-Converting Enzyme 2/chemistry ; Animals ; COVID-19/transmission ; COVID-19 Vaccines/immunology ; Humans ; Mutation ; SARS-CoV-2 ; Spike Glycoprotein, Coronavirus/chemistry ; Spike Glycoprotein, Coronavirus/genetics ; Spike Glycoprotein, Coronavirus/physiology ; Virus Replication
Chemical Substances	COVID-19 Vaccines ; Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2 ; Angiotensin-Converting Enzyme 2 (EC 3.4.17.23)
Language	English
Publishing date	2021-02-04
Publishing country	China
Document type	Journal Article ; Review
ZDB-ID	2546732-3
ISSN	1869-1889 ; 1674-7305
ISSN (online)	1869-1889
ISSN	1674-7305
DOI	10.1007/s11427-020-1893-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence.

Wang, Yan / Tai, Shiwen / Zhang, Shuangquan / Sheng, Nan / Xie, Xuping

Genes

2023 Volume 14, Issue 7

Abstract: Promoters are DNA non-coding regions around the transcription start site and are responsible for regulating the gene transcription process. Due to their key role in gene function and transcriptional activity, the prediction of promoter sequences and ... ...

Abstract	Promoters are DNA non-coding regions around the transcription start site and are responsible for regulating the gene transcription process. Due to their key role in gene function and transcriptional activity, the prediction of promoter sequences and their core elements accurately is a crucial research area in bioinformatics. At present, models based on machine learning and deep learning have been developed for promoter prediction. However, these models cannot mine the deeper biological information of promoter sequences and consider the complex relationship among promoter sequences. In this work, we propose a novel prediction model called PromGER to predict eukaryotic promoter sequences. For a promoter sequence, firstly, PromGER utilizes four types of feature-encoding methods to extract local information within promoter sequences. Secondly, according to the potential relationships among promoter sequences, the whole promoter sequences are constructed as a graph. Furthermore, three different scales of graph-embedding methods are applied for obtaining the global feature information more comprehensively in the graph. Finally, combining local features with global features of sequences, PromGER analyzes and predicts promoter sequences through a tree-based ensemble-learning framework. Compared with seven existing methods, PromGER improved the average specificity of 13%, accuracy of 10%, Matthew's correlation coefficient of 16%, precision of 4%, F1 score of 6%, and AUC of 9%. Specifically, this study interpreted the PromGER by the t-distributed stochastic neighbor embedding (t-SNE) method and SHAPley Additive exPlanations (SHAP) value analysis, which demonstrates the interpretability of the model.
MeSH term(s)	Eukaryota ; Promoter Regions, Genetic ; Eukaryotic Cells ; Computational Biology/methods ; Machine Learning
Language	English
Publishing date	2023-07-13
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2527218-4
ISSN	2073-4425 ; 2073-4425
ISSN (online)	2073-4425
ISSN	2073-4425
DOI	10.3390/genes14071441
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Flavivirus NS4B protein: Structure, function, and antiviral discovery.

Wang, Yan / Xie, Xuping / Shi, Pei-Yong

Antiviral research

2022 Volume 207, Page(s) 105423

Abstract: Infections with mosquito-borne flaviviruses, such as Dengue virus, ZIKV virus, and West Nile virus, pose significant threats to public health. Flaviviruses cause up to 400 million infections each year, leading to many forms of diseases, including fatal ... ...

Abstract	Infections with mosquito-borne flaviviruses, such as Dengue virus, ZIKV virus, and West Nile virus, pose significant threats to public health. Flaviviruses cause up to 400 million infections each year, leading to many forms of diseases, including fatal hemorrhage, encephalitis, congenital abnormalities, and deaths. Currently, there are no clinically approved antiviral drugs for the treatment of flavivirus infections. The non-structural protein NS4B is an emerging target for drug discovery due to its multiple roles in the flaviviral life cycle. In this review, we summarize the latest knowledge on the structure and function of flavivirus NS4B, as well as the progress on antiviral compounds that target NS4B.
MeSH term(s)	Animals ; Antiviral Agents/metabolism ; Antiviral Agents/pharmacology ; Dengue Virus/metabolism ; Flavivirus ; Flavivirus Infections ; Humans ; Zika Virus/metabolism ; Zika Virus Infection
Chemical Substances	Antiviral Agents
Language	English
Publishing date	2022-09-27
Publishing country	Netherlands
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	306628-9
ISSN	1872-9096 ; 0166-3542
ISSN (online)	1872-9096
ISSN	0166-3542
DOI	10.1016/j.antiviral.2022.105423
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Article ; Online: Predictive value of SUVmax in visceral pleural invasive lung adenocarcinoma with different diameters.

Sun, Xiaoyan / Chang, Cheng / Xie, Chun / Zhu, Jiahao / Ni, Xuping / Xie, Wenhui / Wang, Yuetao

Nuclear medicine communications

2023 Volume 44, Issue 11, Page(s) 1020–1028

Abstract: Objectives: This study aimed to investigate predictive visceral pleural invasion (VPI) occurrence value of the maximum standardized uptake value (SUVmax) in patients with lung adenocarcinoma (LA).: Patients and methods: A total of 388 LA patients ... ...

Abstract	Objectives: This study aimed to investigate predictive visceral pleural invasion (VPI) occurrence value of the maximum standardized uptake value (SUVmax) in patients with lung adenocarcinoma (LA). Patients and methods: A total of 388 LA patients were divided into D1ab, D1c, D1, D2, D2a, D2b, D3, and all patient groups based on their tumor diameter (D). Patients were also classified into negative VPI (VPI-n) and positive VPI (VPI-p) groups according to VPI presence. SUVmax of patients was measured with 18F-fluorodeoxyglucose (FDG) by PET/computed tomography (18F-PET/CT). Receiver operating characteristic (ROC) analysis and the area under curve (AUC) of SUVmax were applied to determine optimal cut-off value for predicting VPI occurrence. Results: There were significant differences in SUVmax between VPI-n and VPI-p groups ( P < 0.05) at the same tumor diameter. SUVmax cut-off value and sensitivity (Se,%) of VPI occurrence in each group were following: D1ab was 3.79 [AUC = 0.764, P < 0.001], Se86.11%; D1c was 5.47 (AUC = 0.706, P < 0.001), Se 93.75%; D1 was 5.49 (AUC = 0.731, P < 0.001), Se 79.76%; D2 was 7.36 (AUC = 0.726, P < 0.001), Se81.67%. All patient group was 7.26 (AUC = 0.735, P < 0.001), Se74.19%. Conclusion: In LA patients with the same diameter, SUVmax of the VPI-p group was significantly higher than that of the VPI-n group. The cut-off value of SUVmax for predicting VPI of T1 stage, T1 substages, and T2 stage LA could be determined through ROC curve. SUVmax measurement by PET/CT scan in stratified tumor size is helpful for predicting VPI occurrences of the physician.
Language	English
Publishing date	2023-09-04
Publishing country	England
Document type	Journal Article
ZDB-ID	758141-5
ISSN	1473-5628 ; 0143-3636
ISSN (online)	1473-5628
ISSN	0143-3636
DOI	10.1097/MNM.0000000000001753
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