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  1. Article ; Online: MRNDR: Multihead Attention-Based Recommendation Network for Drug Repurposing.

    Feng, Xin / Ma, Zhansen / Yu, Cuinan / Xin, Ruihao

    Journal of chemical information and modeling

    2024  Volume 64, Issue 7, Page(s) 2654–2669

    Abstract: As is well-known, the process of developing new drugs is extremely expensive, whereas drug repurposing represents a promising approach to augment the efficiency of new drug development. While this method can indeed spare us from expensive drug toxicity ... ...

    Abstract As is well-known, the process of developing new drugs is extremely expensive, whereas drug repurposing represents a promising approach to augment the efficiency of new drug development. While this method can indeed spare us from expensive drug toxicity and safety experiments, it still demands a substantial amount of time to carry out precise efficacy experiments for specific diseases, thereby consuming a significant quantity of resources. Therefore, if we can prescreen potential other indications for selected drugs, it could result in substantial cost savings. In light of this, this paper introduces a drug repurposing recommendation model called MRNDR, which stands for
    MeSH term(s) Humans ; Drug Repositioning/methods ; Algorithms ; Drug-Related Side Effects and Adverse Reactions
    Language English
    Publishing date 2024-02-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c01726
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1230: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: A probabilistic knowledge graph for target identification.

    Liu, Chang / Xiao, Kaimin / Yu, Cuinan / Lei, Yipin / Lyu, Kangbo / Tian, Tingzhong / Zhao, Dan / Zhou, Fengfeng / Tang, Haidong / Zeng, Jianyang

    PLoS computational biology

    2024  Volume 20, Issue 4, Page(s) e1011945

    Abstract: Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On ... ...

    Abstract Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.
    MeSH term(s) Humans ; Computational Biology/methods ; Drug Discovery/methods ; Machine Learning ; Neural Networks, Computer ; Algorithms ; Melanoma ; Probability ; Colorectal Neoplasms
    Language English
    Publishing date 2024-04-05
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1011945
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: SDBA: Score Domain-Based Attention for DNA N4-Methylcytosine Site Prediction from Multiperspectives.

    Xin, Ruihao / Zhang, Fan / Zheng, Jiaxin / Zhang, Yangyi / Yu, Cuinan / Feng, Xin

    Journal of chemical information and modeling

    2023  Volume 64, Issue 7, Page(s) 2839–2853

    Abstract: In tasks related to DNA sequence classification, choosing the appropriate encoding methods is challenging. Some of the methods encode sequences based on prior knowledge that limits the ability of the model to obtain multiperspective information from the ... ...

    Abstract In tasks related to DNA sequence classification, choosing the appropriate encoding methods is challenging. Some of the methods encode sequences based on prior knowledge that limits the ability of the model to obtain multiperspective information from the sequences. We introduced a new trainable ensemble method based on the attention mechanism SDBA, which stands for
    MeSH term(s) DNA/chemistry ; Cytosine/analogs & derivatives
    Chemical Substances DNA (9007-49-2) ; 1-methylcytosine (1122-47-0) ; Cytosine (8J337D1HZY)
    Language English
    Publishing date 2023-08-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c00688
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1230: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Article ; Online: IDDLncLoc: Subcellular Localization of LncRNAs Based on a Framework for Imbalanced Data Distributions.

    Wang, Yan / Zhu, Xiaopeng / Yang, Lili / Hu, Xuemei / He, Kai / Yu, Cuinan / Jiao, Shaoqing / Chen, Jiali / Guo, Rui / Yang, Sen

    Interdisciplinary sciences, computational life sciences

    2022  Volume 14, Issue 2, Page(s) 409–420

    Abstract: Long non-coding RNAs play a crucial role in many life processes of cell, such as genetic markers, RNA splicing, signaling, and protein regulation. Considering that identifying lncRNA's localization in the cell through experimental methods is complicated, ...

    Abstract Long non-coding RNAs play a crucial role in many life processes of cell, such as genetic markers, RNA splicing, signaling, and protein regulation. Considering that identifying lncRNA's localization in the cell through experimental methods is complicated, hard to reproduce, and expensive, we propose a novel method named IDDLncLoc in this paper, which adopts an ensemble model to solve the problem of the subcellular localization. In the proposal model, dinucleotide-based auto-cross covariance features, k-mer nucleotide composition features, and composition, transition, and distribution features are introduced to encode a raw RNA sequence to vector. To screen out reliable features, feature selection through binomial distribution, and recursive feature elimination is employed. Furthermore, strategies of oversampling in mini-batch, random sampling, and stacking ensemble strategies are customized to overcome the problem of data imbalance on the benchmark dataset. Finally, compared with the latest methods, IDDLncLoc achieves an accuracy of 94.96% on the benchmark dataset, which is 2.59% higher than the best method, and the results further demonstrate IDDLncLoc is excellent on the subcellular localization of lncRNA. Besides, a user-friendly web server is available at http://lncloc.club .
    MeSH term(s) Base Sequence ; Computational Biology/methods ; Nucleotides ; Proteins/genetics ; RNA, Long Noncoding/genetics ; RNA, Long Noncoding/metabolism
    Chemical Substances Nucleotides ; Proteins ; RNA, Long Noncoding
    Language English
    Publishing date 2022-02-22
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2493085-4
    ISSN 1867-1462 ; 1913-2751
    ISSN (online) 1867-1462
    ISSN 1913-2751
    DOI 10.1007/s12539-021-00497-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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