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  • Springer Science and Business Media LLC  (4)
  • Yin, Yanbin  (4)
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  • Springer Science and Business Media LLC  (4)
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  • 1
    Online Resource
    Online Resource
    Identification, classification, and evolution of putative xylosyltransferases from algae
    Springer Science and Business Media LLC ; 2019
    In:  Protoplasma Vol. 256, No. 4 ( 2019-7), p. 1119-1132
    Details
    In: Protoplasma, Springer Science and Business Media LLC, Vol. 256, No. 4 ( 2019-7), p. 1119-1132
    Type of Medium: Online Resource
    ISSN: 0033-183X , 1615-6102
    URL: Article
    DOI: 10.1007/s00709-019-01358-2
    RVK:
    WA 15000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 1463033-3
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Antimicrobial peptide identification using multi-scale convolutional network
    Springer Science and Business Media LLC ; 2019
    In:  BMC Bioinformatics Vol. 20, No. 1 ( 2019-12)
    Details
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 20, No. 1 ( 2019-12)
    Abstract: Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN .
    Type of Medium: Online Resource
    ISSN: 1471-2105
    URL: Article
    DOI: 10.1186/s12859-019-3327-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2041484-5
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 3
    Online Resource
    Online Resource
    A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations
    Springer Science and Business Media LLC ; 2021
    In:  BMC Bioinformatics Vol. 22, No. 1 ( 2021-03-21)
    Details
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 22, No. 1 ( 2021-03-21)
    Abstract: Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately. Results We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach. Conclusion Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNA-disease associations. The source code and data are available at https://github.com/zhanglabNKU/VGAELDA .
    Type of Medium: Online Resource
    ISSN: 1471-2105
    URL: Article
    DOI: 10.1186/s12859-021-04073-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2041484-5
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Highly Efficient Photoelectrocatalytic Reduction of CO2 to Methanol by a p–n Heterojunction CeO2/CuO/Cu Catalyst
    Springer Science and Business Media LLC ; 2020
    In:  Nano-Micro Letters Vol. 12, No. 1 ( 2020-12)
    Details
    In: Nano-Micro Letters, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2020-12)
    Abstract: Photoelectrocatalytic reduction of CO 2 to fuels has great potential for reducing anthropogenic CO 2 emissions and also lessening our dependence on fossil fuel energy. Herein, we report the successful development of a novel photoelectrocatalytic catalyst for the selective reduction of CO 2 to methanol, comprising a copper catalyst modified with flower-like cerium oxide nanoparticles (CeO 2 NPs) (a n-type semiconductor) and copper oxide nanoparticles (CuO NPs) (a p-type semiconductor). At an applied potential of − 1.0 V (vs SCE) under visible light irradiation, the CeO 2 NPs/CuO NPs/Cu catalyst yielded methanol at a rate of 3.44 μmol cm −2  h −1 , which was approximately five times higher than that of a CuO NPs/Cu catalyst (0.67 μmol cm −2  h −1 ). The carrier concentration increased by ~ 10 8 times when the flower-like CeO 2 NPs were deposited on the CuO NPs/Cu catalyst, due to synergistic transfer of photoexcited electrons from the conduction band of CuO to that of CeO 2 , which enhanced both photocatalytic and photoelectrocatalytic CO 2 reduction on the CeO 2 NPs. The facile migration of photoexcited electrons and holes across the p–n heterojunction that formed between the CeO 2 and CuO components was thus critical to excellent light-induced CO 2 reduction properties of the CeO 2 NPs/CuO NPs/Cu catalyst. Results encourage the wider application of composite semiconductor electrodes in carbon dioxide reduction.
    Type of Medium: Online Resource
    ISSN: 2311-6706 , 2150-5551
    URL: Article
    DOI: 10.1007/s40820-019-0354-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2642093-4
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