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Research Article

Unveiling the Biomarkers: Identifying Key Signatures for Cancer Hallmarks

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  • @ARTICLE{10.4108/eetpht.10.5649,
        author={Shikha Verma and Aditi Sharan},
        title={Unveiling the Biomarkers: Identifying Key Signatures for Cancer Hallmarks},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={PHAT},
        year={2024},
        month={12},
        keywords={Biomarkers, Hallmark of Cancer, chemical biomarkers, protein biomarkers, karyotype biomarkers, Support Vector machine, Multilabel Classification, Biomedical features, MarkerDB dataset},
        doi={10.4108/eetpht.10.5649}
    }
    
  • Shikha Verma
    Aditi Sharan
    Year: 2024
    Unveiling the Biomarkers: Identifying Key Signatures for Cancer Hallmarks
    PHAT
    EAI
    DOI: 10.4108/eetpht.10.5649
Shikha Verma1,*, Aditi Sharan1
  • 1: Jawaharlal Nehru University
*Contact email: shikha51_scs@jnu.ac.in

Abstract

INTRODUCTION: Finding biomarkers that are closely associated with cancer-related traits is critical to the advancement of cancer research, especially when it comes to personalised treatment. The objective of this research is to explore multiple biomarker categories, including genetics, proteins, and chemicals, in order to better understand the complex terrain of cancer. OBJECTIVES: Few of the objectives include examining a variety of biomarker types, such as chemical, protein, and genetic markers and determining which important biomarker signatures correspond to each cancer hallmark. Also the study aims to perform a comparative analysis to show how the SVM model's features incorporating identified biomarkers improves classification performance. METHODS: The study includes NLP and ML techniques for the identification and classification of biomarkers for the hallmark of cancer dataset. RESULTS: The discovery of important biomarker signatures connected to every cancer hallmark is one of the study's primary findings. In addition, our new SVM-based classification model performed well in the multilabel text classification of PubMed abstracts, showing a significant improvement in performance when the biomarkers were used as features. CONCLUSION: To sum up, this study makes a substantial contribution to the area of cancer research by identifying important biomarker signatures connected to many cancer hallmarks.

Keywords
Biomarkers, Hallmark of Cancer, chemical biomarkers, protein biomarkers, karyotype biomarkers, Support Vector machine, Multilabel Classification, Biomedical features, MarkerDB dataset
Received
2024-12-04
Accepted
2024-12-04
Published
2024-12-04
Publisher
EAI
http://dx.doi.org/10.4108/eetpht.10.5649
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