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  • Oxford University Press (OUP)  (8)
  • 2020-2024  (8)
  • 2022  (8)
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  • Oxford University Press (OUP)  (8)
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  • 2020-2024  (8)
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  • 2022  (8)
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  • 1
    Online Resource
    Online Resource
    Current rice production is highly vulnerable to insect-borne viral diseases
    Oxford University Press (OUP) ; 2022
    In:  National Science Review Vol. 9, No. 9 ( 2022-09-26)
    Details
    In: National Science Review, Oxford University Press (OUP), Vol. 9, No. 9 ( 2022-09-26)
    Type of Medium: Online Resource
    ISSN: 2095-5138 , 2053-714X
    URL: Article
    DOI: 10.1093/nsr/nwac131
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2745465-4
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  • 2
    Online Resource
    Online Resource
    Phylogenomic analysis of Salmonella Indiana ST17, an emerging MDR clonal group in China
    Oxford University Press (OUP) ; 2022
    In:  Journal of Antimicrobial Chemotherapy Vol. 77, No. 11 ( 2022-10-28), p. 2937-2945
    Details
    In: Journal of Antimicrobial Chemotherapy, Oxford University Press (OUP), Vol. 77, No. 11 ( 2022-10-28), p. 2937-2945
    Abstract: To reconstruct the genomic epidemiology and evolution of MDR Salmonella Indiana in China. Methods A total of 108 Salmonella Indiana strains were collected from humans and livestock in China. All isolates were subjected to WGS and antimicrobial susceptibility testing. Phylogenetic relationships and evolutionary analyses were conducted using WGS data from this study and the NCBI database. Results Almost all 108 Salmonella Indiana strains displayed the MDR phenotype. Importantly, 84 isolates possessed concurrent resistance to ciprofloxacin and cefotaxime. WGS analysis revealed that class 1 integrons on the chromosome and IncHI2 plasmids were the key vectors responsible for multiple antibiotic resistance gene (ARG) [including ESBL and plasmid-mediated quinolone resistance (PMQR) genes] transmission among Salmonella Indiana. The 108 Salmonella Indiana dataset displayed a relatively large core genome and ST17 was the predominant ST. Moreover, the global ST17 Salmonella Indiana strains could be divided into five distinct lineages, each of which was significantly associated with a geographical distribution. Genomic analysis revealed multiple antimicrobial resistance determinants and QRDR mutations in Chinese lineages, which almost did not occur in other global lineages. Using molecular clock analysis, we hypothesized that ST17 isolates have existed since 1956 and underwent a major population expansion from the 1980s to the 2000s and the genetic diversity started to decrease around 2011, probably due to geographical barriers, antimicrobial selective pressure and MDR, favouring the establishment of this prevalent multiple antibiotic-resistant lineage and local epidemics. Conclusions This study revealed that adaptation to antimicrobial pressure was possibly pivotal in the recent evolutionary trajectory for the clonal spread of ST17 Salmonella Indiana in China.
    Type of Medium: Online Resource
    ISSN: 0305-7453 , 1460-2091
    URL: Article
    DOI: 10.1093/jac/dkac243
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1467478-6
    SSG: 15,3
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  • 3
    Online Resource
    Online Resource
    IHGC-GAN: influence hypergraph convolutional generative adversarial network for risk prediction of late mild cognitive impairment based on imaging genetic data
    Oxford University Press (OUP) ; 2022
    In:  Briefings in Bioinformatics Vol. 23, No. 3 ( 2022-05-13)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 3 ( 2022-05-13)
    Abstract: Predicting disease progression in the initial stage to implement early intervention and treatment can effectively prevent the further deterioration of the condition. Traditional methods for medical data analysis usually fail to perform well because of their incapability for mining the correlation pattern of pathogenies. Therefore, many calculation methods have been excavated from the field of deep learning. In this study, we propose a novel method of influence hypergraph convolutional generative adversarial network (IHGC-GAN) for disease risk prediction. First, a hypergraph is constructed with genes and brain regions as nodes. Then, an influence transmission model is built to portray the associations between nodes and the transmission rule of disease information. Third, an IHGC-GAN method is constructed based on this model. This method innovatively combines the graph convolutional network (GCN) and GAN. The GCN is used as the generator in GAN to spread and update the lesion information of nodes in the brain region-gene hypergraph. Finally, the prediction accuracy of the method is improved by the mutual competition and repeated iteration between generator and discriminator. This method can not only capture the evolutionary pattern from early mild cognitive impairment (EMCI) to late MCI (LMCI) but also extract the pathogenic factors and predict the deterioration risk from EMCI to LMCI. The results on the two datasets indicate that the IHGC-GAN method has better prediction performance than the advanced methods in a variety of indicators.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbac093
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    A novel generation adversarial network framework with characteristics aggregation and diffusion for brain disease classification and feature selection
    Oxford University Press (OUP) ; 2022
    In:  Briefings in Bioinformatics Vol. 23, No. 6 ( 2022-11-19)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 6 ( 2022-11-19)
    Abstract: Imaging genetics provides unique insights into the pathological studies of complex brain diseases by integrating the characteristics of multi-level medical data. However, most current imaging genetics research performs incomplete data fusion. Also, there is a lack of effective deep learning methods to analyze neuroimaging and genetic data jointly. Therefore, this paper first constructs the brain region-gene networks to intuitively represent the association pattern of pathogenetic factors. Second, a novel feature information aggregation model is constructed to accurately describe the information aggregation process among brain region nodes and gene nodes. Finally, a deep learning method called feature information aggregation and diffusion generative adversarial network (FIAD-GAN) is proposed to efficiently classify samples and select features. We focus on improving the generator with the proposed convolution and deconvolution operations, with which the interpretability of the deep learning framework has been dramatically improved. The experimental results indicate that FIAD-GAN can not only achieve superior results in various disease classification tasks but also extract brain regions and genes closely related to AD. This work provides a novel method for intelligent clinical decisions. The relevant biomedical discoveries provide a reliable reference and technical basis for the clinical diagnosis, treatment and pathological analysis of disease.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbac454
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    Feature aggregation graph convolutional network based on imaging genetic data for diagnosis and pathogeny identification of Alzheimer’s disease
    Oxford University Press (OUP) ; 2022
    In:  Briefings in Bioinformatics Vol. 23, No. 3 ( 2022-05-13)
    Details
    In: Briefings in Bioinformatics, Oxford University Press (OUP), Vol. 23, No. 3 ( 2022-05-13)
    Abstract: The roles of brain regions activities and gene expressions in the development of Alzheimer’s disease (AD) remain unclear. Existing imaging genetic studies usually has the problem of inefficiency and inadequate fusion of data. This study proposes a novel deep learning method to efficiently capture the development pattern of AD. First, we model the interaction between brain regions and genes as node-to-node feature aggregation in a brain region–gene network. Second, we propose a feature aggregation graph convolutional network (FAGCN) to transmit and update the node feature. Compared with the trivial graph convolutional procedure, we replace the input from the adjacency matrix with a weight matrix based on correlation analysis and consider common neighbor similarity to discover broader associations of nodes. Finally, we use a full-gradient saliency graph mechanism to score and extract the pathogenetic brain regions and risk genes. According to the results, FAGCN achieved the best performance among both traditional and cutting-edge methods and extracted AD-related brain regions and genes, providing theoretical and methodological support for the research of related diseases.
    Type of Medium: Online Resource
    ISSN: 1467-5463 , 1477-4054
    URL: Article
    DOI: 10.1093/bib/bbac137
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2036055-1
    SSG: 12
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  • 6
    Online Resource
    Online Resource
    Clustering-Evolutionary Random Support Vector Machine Ensemble for fMRI-Based Asperger Syndrome Diagnosis
    Oxford University Press (OUP) ; 2022
    In:  The Computer Journal Vol. 65, No. 2 ( 2022-02-14), p. 251-260
    Details
    In: The Computer Journal, Oxford University Press (OUP), Vol. 65, No. 2 ( 2022-02-14), p. 251-260
    Abstract: It is a hot spot in the field of computer application to diagnose complex brain diseases such as Asperger syndrome (AS) using machine learning technology. To identify AS patients and detect lesions, this paper proposes a novel clustering-evolutionary random support vector machine (SVM) ensemble (CERSVME) based on graph theory. Firstly, we extract graph theory indexes from the resting-state functional magnetic resonance imaging (fMRI) data as sample features and construct an ensemble learner by integrating multiple SVM classifiers. Secondly, the base learners with high redundancy and poor classification ability are deleted through clustering evolutions to improve the performance of the model. Then the CERSVME model is used to classify fMRI image of AS patients and healthy controls. According to the classification results, a multi-stage analysis scheme is designed to find the AS-related brain areas. We validate the proposed approach on 135 participants from autism brain imaging data exchange cohort. The highest accuracy reported by the CERSVME reaches 95.24%. More importantly, the diseased brain areas such as middle frontal gyrus, hippocampus and precuneus are found based on their contributions to classification performances of the CERSVME. Our study provides useful assistances for the clinical detection of patients with AS.
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxaa023
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1477172-X
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  • 7
    Online Resource
    Online Resource
    HCDT: an integrated highly confident drug–target resource
    Oxford University Press (OUP) ; 2022
    In:  Database Vol. 2022 ( 2022-11-24)
    Details
    In: Database, Oxford University Press (OUP), Vol. 2022 ( 2022-11-24)
    Abstract: Drug–target association plays an important role in drug discovery, drug repositioning, drug synergy prediction, etc. Currently, a lot of drug-related databases, such as DrugBank and BindingDB, have emerged. However, these databases are separate, incomplete and non-uniform with different criteria. Here, we integrated eight drug-related databases; collected, filtered and supplemented drugs, target genes and experimentally validated (highly confident) associations and built a highly confident drug–target (HCDT: http://hainmu-biobigdata.com/hcdt) database. HCDT database includes 500 681 HCDT associations between 299 458 drugs and 5618 target genes. Compared to individual databases, HCDT database contains 1.1 to 254.2 times drugs, 1.8–5.5 times target genes and 1.4–27.7 times drug–target associations. It is normative, publicly available and easy for searching, browsing and downloading. Together with multi-omics data, it will be a good resource in analyzing the drug functional mechanism, mining drug-related biological pathways, predicting drug synergy, etc. Database URL: http://hainmu-biobigdata.com/hcdt
    Type of Medium: Online Resource
    ISSN: 1758-0463
    URL: Article
    DOI: 10.1093/database/baac101
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2496706-3
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  • 8
    Online Resource
    Online Resource
    Stochastically multimerized ParB orchestrates DNA assembly as unveiled by single-molecule analysis
    Oxford University Press (OUP) ; 2022
    In:  Nucleic Acids Research Vol. 50, No. 16 ( 2022-09-09), p. 9294-9305
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 50, No. 16 ( 2022-09-09), p. 9294-9305
    Abstract: The tripartite ParABS system mediates chromosome segregation in a wide range of bacteria. Dimeric ParB was proposed to nucleate on parS sites and spread to neighboring DNA. However, how properly distributed ParB dimers further compact chromosomal DNA into a higher-order nucleoprotein complex for partitioning remains poorly understood. Here, using a single-molecule approach, we show that tens of Bacillus subtilis ParB (Spo0J) proteins can stochastically multimerize on and stably bind to nonspecific DNA. The introduction of CTP promotes the formation and diffusion of the multimeric ParB along DNA, offering an opportunity for ParB proteins to further forgather and cluster. Intriguingly, ParB multimers can recognize parS motifs and are more inclined to remain immobile on them. Importantly, the ParB multimer features distinct capabilities of not only bridging two independent DNA molecules but also mediating their transportation, both of which are enhanced by the presence of either CTP or parS in the DNA. These findings shed new light on ParB dynamics in self-multimerization and DNA organization and help to better comprehend the assembly of the ParB-DNA partition complex.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkac651
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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