DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture

By Alok Sharma, Edwin Vans, Daichi Shigemizu, Keith A. Boroevich, Tatsuhiko Tsunoda. Scientific Reports, 2019

393 citations recorded by the source corpus.

It is critical, but difficult, to catch the small variation in genomic or other kinds of data that differentiates phenotypes or categories. A plethora of data is available, but the information from its genes or elements is spread over arbitrarily, making it challenging to extract relevant details for identification. However, an arrangement of similar genes into clusters makes these differences more accessible and allows for robust identification of hidden mechanisms (e.g. pathways) than dealing with elements individually. Here we propose, DeepInsight, which converts non-image samples into a well-organized image-form. Thereby, the power of convolution neural network (CNN), including GPU utilization, can be realized for non-image samples. Furthermore, DeepInsight enables feature extraction through the application of CNN for non-image samples to seize imperative information and shown promising results. To our knowledge, this is the first work to apply CNN simultaneously on different kinds of non-image datasets: RNA-seq, vowels, text, and artificial.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d292dcf03ee8b565ffe2f5914e44844f7112d2e6

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In Molecular Biology

1. Amina Bolatkan
2. Ryuji Hamamoto
3. Yusuke Nakamura
4. N. Dohmae
5. Takehiro Suzuki
6. Makoto Nakakido

Citations in the corpus

• Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations (2020, background): “When DNA base sequences are given as input, deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can predict some functional activities based on the sequential information [15–19].”