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  • Similarity Computation based on Formal Concept Analysis for Colorectal Cancer Patients

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    Zhang461424-Accepted.pdf (443.4Kb)
    Author(s)
    Xiang, J
    Xu, H
    Pokharel, S
    Li, J
    Xue, F
    Zhang, P
    Griffith University Author(s)
    Zhang, Ping
    Year published
    2020
    Metadata
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    Abstract
    Colorectal cancer is a heterogeneous disease. Its response to targeted therapies is associated with various factors, and the treatment effect differ significantly between individuals. Personalize medical treatment (PMT), which takes into consideration of individual patient characteristics, is the most effective way to deal with this issue. Patient similarity and clustering analysis is an important part in PMT. Earlier works mainly focused on similarity computation among the patients but overlook to preserve relationships. This paper presents a formal concept analysis-based approach for computing the similarity between ...
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    Colorectal cancer is a heterogeneous disease. Its response to targeted therapies is associated with various factors, and the treatment effect differ significantly between individuals. Personalize medical treatment (PMT), which takes into consideration of individual patient characteristics, is the most effective way to deal with this issue. Patient similarity and clustering analysis is an important part in PMT. Earlier works mainly focused on similarity computation among the patients but overlook to preserve relationships. This paper presents a formal concept analysis-based approach for computing the similarity between colorectal cancer patients. The approach not only does the clustering of patients based on their similarity but also can preserve the relations between clusters in hierarchical structural form. This would allow us to build a knowledge base which is helpful for clinicians to take fast and effective decision for treatment and care of colorectal cancer patient.
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    Conference Title
    Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
    DOI
    https://doi.org/10.1109/BIBM49941.2020.9313144
    Copyright Statement
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Oncology and Carcinogenesis
    Publication URI
    http://hdl.handle.net/10072/402092
    Collection
    • Conference outputs

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