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  1. Article: Erratum: Optical parameters estimation in inhomogeneous turbid media using backscattered light: for transcutaneous scattering measurement of intravascular blood: erratum.

    Liang, Shiyang / Miyake, Takeo / Shimizu, Koichi

    Biomedical optics express

    2024  Volume 15, Issue 2, Page(s) 910

    Abstract: This corrects the article on p. 237 in vol. 15, PMID: 38223194.]. ...

    Abstract [This corrects the article on p. 237 in vol. 15, PMID: 38223194.].
    Language English
    Publishing date 2024-01-19
    Publishing country United States
    Document type Published Erratum
    ZDB-ID 2572216-5
    ISSN 2156-7085
    ISSN 2156-7085
    DOI 10.1364/BOE.517856
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Optical parameters estimation in inhomogeneous turbid media using backscattered light: for transcutaneous scattering measurement of intravascular blood.

    Liang, Shiyang / Miyake, Takeo / Shimizu, Koichi

    Biomedical optics express

    2023  Volume 15, Issue 1, Page(s) 237–255

    Abstract: In our earlier research, a technique was developed to estimate the effective attenuation coefficient of subcutaneous blood vessels from the skin surface using the spatial distribution of backscattered near-infrared (NIR) light. The scattering effect in ... ...

    Abstract In our earlier research, a technique was developed to estimate the effective attenuation coefficient of subcutaneous blood vessels from the skin surface using the spatial distribution of backscattered near-infrared (NIR) light. The scattering effect in surrounding tissues was suppressed through the application of a differential principle, provided that the
    Language English
    Publishing date 2023-12-15
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2572216-5
    ISSN 2156-7085
    ISSN 2156-7085
    DOI 10.1364/BOE.510245
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Sequence pre-training-based graph neural network for predicting lncRNA-miRNA associations.

    Wang, Zixiao / Liang, Shiyang / Liu, Siwei / Meng, Zhaohan / Wang, Jingjie / Liang, Shangsong

    Briefings in bioinformatics

    2023  Volume 24, Issue 5

    Abstract: MicroRNAs (miRNAs) silence genes by binding to messenger RNAs, whereas long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) that can relieve miRNA silencing effects and upregulate target gene expression. The ceRNA association ... ...

    Abstract MicroRNAs (miRNAs) silence genes by binding to messenger RNAs, whereas long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) that can relieve miRNA silencing effects and upregulate target gene expression. The ceRNA association between lncRNAs and miRNAs has been a research hotspot due to its medical importance, but it is challenging to verify experimentally. In this paper, we propose a novel deep learning scheme, i.e. sequence pre-training-based graph neural network (SPGNN), that combines pre-training and fine-tuning stages to predict lncRNA-miRNA associations from RNA sequences and the existing interactions represented as a graph. First, we utilize a sequence-to-vector technique to generate pre-trained embeddings based on the sequences of all RNAs during the pre-training stage. In the fine-tuning stage, we use Graph Neural Network to learn node representations from the heterogeneous graph constructed using lncRNA-miRNA association information. We evaluate our proposed scheme SPGNN on our newly collected animal lncRNA-miRNA association dataset and demonstrate that combining the $k$-mer technique and Doc2vec model for pre-training with the Simple Graph Convolution Network for fine-tuning is effective in predicting lncRNA-miRNA associations. Our approach outperforms state-of-the-art baselines across various evaluation metrics. We also conduct an ablation study and hyperparameter analysis to verify the effectiveness of each component and parameter of our scheme. The complete code and dataset are available on GitHub: https://github.com/zixwang/SPGNN.
    MeSH term(s) Animals ; MicroRNAs/genetics ; RNA, Long Noncoding/genetics ; Benchmarking ; Neural Networks, Computer ; RNA, Messenger
    Chemical Substances MicroRNAs ; RNA, Long Noncoding ; RNA, Messenger
    Language English
    Publishing date 2023-08-31
    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/bbad317
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction.

    Liang, Shiyang / Liu, Siwei / Song, Junliang / Lin, Qiang / Zhao, Shihong / Li, Shuaixin / Li, Jiahui / Liang, Shangsong / Wang, Jingjie

    BMC bioinformatics

    2023  Volume 24, Issue 1, Page(s) 335

    Abstract: Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional experimental methods for circRNA-disease associations ... ...

    Abstract Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional experimental methods for circRNA-disease associations identification are labor-intensive. In this work, we propose a novel method based on the heterogeneous graph neural network and metapaths for circRNA-disease associations prediction termed as HMCDA. First, a heterogeneous graph consisting of circRNA-disease associations, circRNA-miRNA associations, miRNA-disease associations and disease-disease associations are constructed. Then, six metapaths are defined and generated according to the biomedical pathways. Afterwards, the entity content transformation, intra-metapath and inter-metapath aggregation are implemented to learn the embeddings of circRNA and disease entities. Finally, the learned embeddings are used to predict novel circRNA-disase associations. In particular, the result of extensive experiments demonstrates that HMCDA outperforms four state-of-the-art models in fivefold cross validation. In addition, our case study indicates that HMCDA has the ability to identify novel circRNA-disease associations.
    MeSH term(s) RNA, Circular ; Research Design ; Learning ; MicroRNAs/genetics ; Neural Networks, Computer
    Chemical Substances RNA, Circular ; MicroRNAs
    Language English
    Publishing date 2023-09-11
    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/s12859-023-05441-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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