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A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages

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Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13810))

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Abstract

Standard machine learning algorithms have limited knowledge extraction capability in discriminating cancer stages based on genomic characterizations, due to the strongly correlated nature of high-dimensional genomic data. Moreover, activation of pathways plays a crucial role in the growth and progression of cancer from early-stage to late-stage. That is why we implemented a kernel-based neural network framework that integrates pathways and gene expression data using multiple kernels and discriminates early- and late-stages of cancers. Our goal is to identify the relevant molecular mechanisms of the biological processes which might be driving cancer progression. As the input of developed multilayer perceptron (MLP), we constructed kernel matrices on multiple views of expression profiles of primary tumors extracted from pathways. We used Hallmark and Pathway Interaction Database (PID) datasets to restrict the search area to interpretable solutions. We applied our algorithm to 12 cancer cohorts from the Cancer Genome Atlas (TCGA), including more than 5100 primary tumors. The results showed that our algorithm could extract meaningful and disease-specific mechanisms of cancers. We tested the predictive performance of our MLP algorithm and compared it against three existing classification algorithms, namely, random forests, support vector machines, and multiple kernel learning. Our MLP method obtained better or comparable predictive performance against these algorithms.

Our implementation of proposed algorithm in R is available at https://github.com/MSoleimanpoor/Neural-Network.git.

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Author information

Authors and Affiliations

  1. Graduate School of Sciences and Engineering, Koç University, Istanbul, Turkey

    Marzieh Soleimanpoor

  2. Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada

    Milad Mokhtaridoost

  3. Department of Industrial Engineering, College of Engineering, Koç University, Istanbul, Turkey

    Mehmet Gönen

  4. School of Medicine, Koç University, İstanbul, 34450, Turkey

    Mehmet Gönen

Authors
  1. Marzieh Soleimanpoor
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  2. Milad Mokhtaridoost
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  3. Mehmet Gönen
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Corresponding author

Correspondence to Milad Mokhtaridoost .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Soleimanpoor, M., Mokhtaridoost, M., Gönen, M. (2023). A Kernel-Based Multilayer Perceptron Framework to Identify Pathways Related to Cancer Stages. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13810. Springer, Cham. https://doi.org/10.1007/978-3-031-25599-1_6

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  • DOI: https://doi.org/10.1007/978-3-031-25599-1_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25598-4

  • Online ISBN: 978-3-031-25599-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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