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  1. Article ; Online: Artificial intelligence is transforming the research paradigm of environmental science and engineering

    Xu Wang / Guangtao Fu / Nan-Qi Ren

    Environmental Science and Ecotechnology, Vol 19, Iss , Pp 100346- (2024)

    2024  

    Keywords Environmental sciences ; GE1-350 ; Environmental technology. Sanitary engineering ; TD1-1066
    Language English
    Publishing date 2024-05-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.

    Qi, Ren / Zou, Quan

    Research (Washington, D.C.)

    2023  Volume 6, Page(s) 50

    Abstract: Cancer treatments always face challenging problems, particularly drug resistance due to tumor cell heterogeneity. The existing datasets include the relationship between gene expression and drug sensitivities; however, the majority are based on tissue- ... ...

    Abstract Cancer treatments always face challenging problems, particularly drug resistance due to tumor cell heterogeneity. The existing datasets include the relationship between gene expression and drug sensitivities; however, the majority are based on tissue-level studies. Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy. Fortunately, machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression. Good modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments. In this paper, we review machine learning and deep learning approaches in drug research. By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis, we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level. We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.
    Language English
    Publishing date 2023-03-09
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 2639-5274
    ISSN (online) 2639-5274
    DOI 10.34133/research.0050
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Editorial: Machine learning methods in single-cell immune and drug response prediction.

    Qi, Ren / Zou, Quan

    Frontiers in genetics

    2023  Volume 14, Page(s) 1233078

    Language English
    Publishing date 2023-06-19
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2023.1233078
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Special Protein or RNA Molecules Computational Identification.

    Qi, Ren / Zou, Quan

    International journal of molecular sciences

    2023  Volume 24, Issue 14

    Abstract: The identification of special protein or RNA molecules via computational methods is of great importance in understanding their biological functions and developing new treatments for diseases [ ... ]. ...

    Abstract The identification of special protein or RNA molecules via computational methods is of great importance in understanding their biological functions and developing new treatments for diseases [...].
    MeSH term(s) Proteins ; RNA/genetics ; RNA/metabolism ; Computational Biology
    Chemical Substances Proteins ; RNA (63231-63-0)
    Language English
    Publishing date 2023-07-11
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms241411312
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: By the power of young researchers

    Nan-Qi Ren

    Environmental Science and Ecotechnology, Vol 8, Iss , Pp 100120- (2021)

    2021  

    Keywords Environmental sciences ; GE1-350 ; Environmental technology. Sanitary engineering ; TD1-1066
    Language English
    Publishing date 2021-10-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: PseU-KeMRF: A novel method for identifying RNA pseudouridine sites.

    Chen, Mingshuai / Zou, Quan / Qi, Ren / Ding, Yijie

    IEEE/ACM transactions on computational biology and bioinformatics

    2024  Volume PP

    Abstract: Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of ... ...

    Abstract Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of various biological mechanisms of pseudouridine. However, the use of traditional experimental methods faces certain challenges. The development of fast and convenient computational methods is necessary to accurately identify pseudouridine sites from RNA sequence information. To address this, we introduce a novel pseudouridine site prediction model called PseU-KeMRF, which can identify pseudouridine sites in three species, H. sapiens, S. cerevisiae, and M. musculus. Through comprehensive analysis, we selected four RNA coding schemes, including binary feature, position-specific trinucleotide propensity based on single strand (PSTNPss), nucleotide chemical property (NCP) and pseudo k-tuple composition (PseKNC). Then the support vector machine-recursive feature elimination (SVM-RFE) method was used for feature selection and the feature subset was optimized. Finally, the best feature subsets are input into the kernel based on multinomial random forests (KeMRF) classifier for cross-validation and independent testing. As a new classification method, compared with the traditional random forest, KeMRF not only improves the node splitting process of decision tree construction based on multinomial distribution, but also combines the easy to interpret kernel method for prediction, which makes the classification performance better. Our results indicate superior predictive performance of PseU-KeMRF over other existing models. On the three species' training datasets, the testing accuracy of PseU-KeMRF was 0.66%, 3.66%, and 2.76% higher, respectively, than the best available methods. Moreover, PseU-KeMRF's accuracy on independent testing datasets was 15.15% and 11.0% higher, respectively, than the best available methods. The above results can prove that PseU-KeMRF is a highly competitive predictive model that can successfully identify pseudouridine sites in RNA sequences.
    Language English
    Publishing date 2024-04-16
    Publishing country United States
    Document type Journal Article
    ISSN 1557-9964
    ISSN (online) 1557-9964
    DOI 10.1109/TCBB.2024.3389094
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Publishing a sound paper of environmental science and ecological technology

    Shih-Hsin Ho / Nan-Qi Ren

    Environmental Science and Ecotechnology, Vol 13, Iss , Pp 100242- (2023)

    Some experiences and tips for young researchers

    2023  

    Keywords Environmental sciences ; GE1-350 ; Environmental technology. Sanitary engineering ; TD1-1066
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Introduction to the special column ‘Policy Outlook’

    Nan-Qi Ren

    Environmental Science and Ecotechnology, Vol 1, Iss , Pp 100015- (2020)

    2020  

    Keywords Environmental sciences ; GE1-350 ; Environmental technology. Sanitary engineering ; TD1-1066
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: CircRNA identification and feature interpretability analysis.

    Niu, Mengting / Wang, Chunyu / Chen, Yaojia / Zou, Quan / Qi, Ren / Xu, Lei

    BMC biology

    2024  Volume 22, Issue 1, Page(s) 44

    Abstract: Background: Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining ... ...

    Abstract Background: Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation.
    Results: We developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification.
    Conclusions: CircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git .
    MeSH term(s) Humans ; RNA, Circular/genetics ; MicroRNAs ; Neural Networks, Computer ; Neoplasms/genetics ; Computational Biology/methods
    Chemical Substances RNA, Circular ; MicroRNAs
    Language English
    Publishing date 2024-02-27
    Publishing country England
    Document type Journal Article
    ZDB-ID 2133020-7
    ISSN 1741-7007 ; 1741-7007
    ISSN (online) 1741-7007
    ISSN 1741-7007
    DOI 10.1186/s12915-023-01804-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: A new method for handling heterogeneous data in bioinformatics.

    Qi, Ren / Zhang, Zehua / Wu, Jin / Dou, Lijun / Xu, Lei / Cheng, Yue

    Computers in biology and medicine

    2024  Volume 170, Page(s) 107937

    Abstract: Heterogeneous data, especially a mixture of numerical and categorical data, widely exist in bioinformatics. Most of works focus on defining new distance metrics rather than learning discriminative metrics for mixed data. Here, we create a new support ... ...

    Abstract Heterogeneous data, especially a mixture of numerical and categorical data, widely exist in bioinformatics. Most of works focus on defining new distance metrics rather than learning discriminative metrics for mixed data. Here, we create a new support vector heterogeneous metric learning framework for mixed data. A heterogeneous sample pair kernel is defined for mixed data and metric learning is then converted to a sample pair classification problem. The suggested approach lends itself well to effective resolution through conventional support vector machine solvers. Empirical assessments conducted on mixed data benchmarks and cancer datasets affirm the exceptional efficacy demonstrated by the proposed modeling technique.
    MeSH term(s) Algorithms ; Computational Biology ; Support Vector Machine
    Language English
    Publishing date 2024-01-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2024.107937
    Database MEDical Literature Analysis and Retrieval System OnLINE

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