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Genomic Data Machined: The Random Forest Algorithm for Discovering Breast Cancer Biomarkers

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Information and Communication Technologies and Sustainable Development (ICT&SD 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 809))

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Abstract

Advanced data analysis tools and bioinformatics are essential for uncovering the nature of breast cancer, which is the leading cause of cancer death among women. The goal of this study is to identify potential genomic biomarkers that have a significant impact on four prognostic factors, including tumour size, lymph node involvement, metastasis, and overall survival status. The Random Forest algorithm has been trained on data from The Cancer Genome Atlas Breast Cancer, which contains the expression values of 19,737 genes. In order to obtain the optimal learning model, the process has been repeated 20 times for each indicator, and only the genes with a p-value < 0.05 were taken into further consideration. Several performance metrics (e.g., F1 score) were calculated to check the algorithm's reliability. As a result, 97 and 7 genes were included in the extended and final databases, respectively. The chosen genes have been proven to play a critical role in cancer-related pathways, such as Toll-like receptor and NF-κB, and have effects on cell proliferation, tumour formation, and angiogenesis. Thus, this study demonstrates the potential of machine learning analyses for biomedical purposes and provides machine-generated insights into breast cancer development, setting the groundwork for further in vitro examinations to validate the prognostic potential of these biomarkers.

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Acknowledgements

This work was partly supported by EMBO IG 4728-2020, Jacek Arct and ‘New Technologies for Women’ scholarshipsб Kyiv School of Economics «Talents for Ukraine» for NK and Alexander von Humboldt Stiftung (Philipp Schwartz-Initiative) for HF.

Author information

Authors and Affiliations

  1. Adam Mickiewicz University, Uniwersytetu Poznańskiego Str. 6, 60-780, Poznań, Poland

    Nadiia Kasianchuk

  2. Bogomolets National Medical University, Taras Shevchenko Street 13, Kyiv, 01601, Ukraine

    Nadiia Kasianchuk

  3. Taras Shevchenko National University of Kyiv, Illyenka Street 36, Kyiv, 01601, Ukraine

    Dmytro Tsvyk

  4. Anhalt University of Applied Sciences, Bernburger Str. 57, 06366, Köthen, Germany

    Eduard Siemens

  5. National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Beresteiskyi Ave. 37, Kyiv, 03056, Ukraine

    Vladyslav Ostash

  6. University of Rostock, Albert Einstein Str. 3, 19059, Rostock, Germany

    Halina Falfushynska

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  1. Nadiia Kasianchuk
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  2. Dmytro Tsvyk
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  3. Eduard Siemens
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  4. Vladyslav Ostash
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  5. Halina Falfushynska
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Correspondence to Nadiia Kasianchuk .

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Editors and Affiliations

  1. Institute of Telecommunications and Global Information Space of NAS of Ukraine, Kyiv, Ukraine

    Stanislav Dovgyi

  2. Institute of Telecommunications and Global Information Space of NAS of Ukraine, Kyiv, Ukraine

    Oleksandr Trofymchuk

  3. Institute of Telecommunications and Global Information Space of NAS of Ukraine, Kyiv, Ukraine

    Vasyl Ustimenko

  4. “Igor Sikorsky Kyiv Polytechnic Institute”, National Technical University of Ukraine, Kyiv, Ukraine

    Larysa Globa

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Kasianchuk, N., Tsvyk, D., Siemens, E., Ostash, V., Falfushynska, H. (2023). Genomic Data Machined: The Random Forest Algorithm for Discovering Breast Cancer Biomarkers. In: Dovgyi, S., Trofymchuk, O., Ustimenko, V., Globa, L. (eds) Information and Communication Technologies and Sustainable Development. ICT&SD 2022. Lecture Notes in Networks and Systems, vol 809. Springer, Cham. https://doi.org/10.1007/978-3-031-46880-3_25

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