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  1. Artikel: [Clinicopathological features and prognostic factors of patients with lung metastasis of stage Ⅰa~Ⅲb cervical cancer].

    Liu, H / Zhang, G N / Luo, M / Zhang, X D / Fan, Y / Peng, C R

    Zhonghua zhong liu za zhi [Chinese journal of oncology

    2023  Band 45, Heft 4, Seite(n) 340–347

    Abstract: Objective: ...

    Abstract Objective:
    Mesh-Begriff(e) Female ; Humans ; Lung Neoplasms/pathology ; Lung Neoplasms/secondary ; Lung Neoplasms/therapy ; Neoplasm Staging ; Prognosis ; Retrospective Studies ; Uterine Cervical Neoplasms/pathology ; Uterine Cervical Neoplasms/therapy ; Survival Analysis
    Sprache Chinesisch
    Erscheinungsdatum 2023-04-05
    Erscheinungsland China
    Dokumenttyp English Abstract ; Journal Article
    ZDB-ID 603424-x
    ISSN 0253-3766
    ISSN 0253-3766
    DOI 10.3760/cma.j.cn112152-20211230-00984
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel: [Analysis of factors related to the prognostic benefit of neoadjuvant chemotherapy followed by interval debulking surgery in patients with advanced ovarian cancer].

    Wang, D F / Zhang, G N / Peng, C R / Shi, Y / Shi, X W

    Zhonghua fu chan ke za zhi

    2021  Band 56, Heft 6, Seite(n) 385–392

    Abstract: Objective: ...

    Abstract Objective:
    Mesh-Begriff(e) Adult ; Carcinoma, Ovarian Epithelial/drug therapy ; Carcinoma, Ovarian Epithelial/surgery ; Chemotherapy, Adjuvant ; Cytoreduction Surgical Procedures ; Female ; Humans ; Middle Aged ; Neoadjuvant Therapy ; Neoplasm Staging ; Ovarian Neoplasms/drug therapy ; Ovarian Neoplasms/surgery ; Prognosis ; Retrospective Studies
    Sprache Chinesisch
    Erscheinungsdatum 2021-06-21
    Erscheinungsland China
    Dokumenttyp Journal Article
    ZDB-ID 604570-4
    ISSN 0529-567X
    ISSN 0529-567X
    DOI 10.3760/cma.j.cn112141-20201207-00871
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Prediction of RNA-binding proteins by voting systems.

    Peng, C R / Liu, L / Niu, B / Lv, Y L / Li, M J / Yuan, Y L / Zhu, Y B / Lu, W C / Cai, Y D

    Journal of biomedicine & biotechnology

    2011  Band 2011, Seite(n) 506205

    Abstract: It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to ... ...

    Abstract It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS) is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy) value of 79.72% and MCC (Matthew's correlation coefficient) value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance) strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier's vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.
    Mesh-Begriff(e) Algorithms ; Artificial Intelligence ; Computational Biology ; Databases, Protein ; Molecular Sequence Annotation/methods ; RNA/chemistry ; RNA-Binding Proteins/chemistry ; RNA-Binding Proteins/genetics ; RNA-Binding Proteins/metabolism ; Sequence Analysis, Protein/methods
    Chemische Substanzen RNA-Binding Proteins ; RNA (63231-63-0)
    Sprache Englisch
    Erscheinungsdatum 2011-07-26
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2052552-7
    ISSN 1110-7251 ; 1110-7243
    ISSN (online) 1110-7251
    ISSN 1110-7243
    DOI 10.1155/2011/506205
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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