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Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks

Author

Listed:
  • Omid Bazgir

    (Texas Tech University)

  • Ruibo Zhang

    (Texas Tech University)

  • Saugato Rahman Dhruba

    (Texas Tech University)

  • Raziur Rahman

    (Texas Tech University)

  • Souparno Ghosh

    (Texas Tech University
    University of Nebraska-Lincoln)

  • Ranadip Pal

    (Texas Tech University)

Abstract

Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation approach termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) to arrange high-dimensional vectors in a compact image form conducible for CNN-based deep learning. We consider the similarities between features to generate a concise feature map in the form of a two-dimensional image by minimizing the pairwise distance values following a Bayesian Metric Multidimensional Scaling Approach. We hypothesize that this approach enables embedded feature extraction and, integrated with CNN-based deep learning, can boost the predictive accuracy. We illustrate the superior predictive capabilities of the proposed framework as compared to state-of-the-art methodologies in drug sensitivity prediction scenarios using synthetic datasets, drug chemical descriptors as predictors from NCI60, and both transcriptomic information and drug descriptors as predictors from GDSC.

Suggested Citation

  • Omid Bazgir & Ruibo Zhang & Saugato Rahman Dhruba & Raziur Rahman & Souparno Ghosh & Ranadip Pal, 2020. "Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18197-y
    DOI: 10.1038/s41467-020-18197-y
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    Cited by:

    1. Md Tauhidul Islam & Lei Xing, 2023. "Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data," Nature Communications, Nature, vol. 14(1), pages 1-17, December.

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