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

Omid Bazgir, Ruibo Zhang, Saugato Rahman Dhruba, Raziur Rahman, Souparno Ghosh and Ranadip Pal ()
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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
Ranadip Pal: Texas Tech University

Nature Communications, 2020, vol. 11, issue 1, 1-13

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18197-y

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