Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A novel method for classification of tabular data using convolutional neural networks

Ljubomir Buturović, Dejan Miljković
doi: https://doi.org/10.1101/2020.05.02.074203
Ljubomir Buturović
Inflammatix Inc., Burlingame, CA 94010
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: lbuturovic@inflammatix.com
Dejan Miljković
Inflammatix Inc., Burlingame, CA 94010
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

ABSTRACT

Convolutional neural networks (CNNs) represent a major breakthrough in image classification. However, there has not been similar progress in applying CNNs, or neural networks of any kind, to classification of tabular data. We developed and evaluated a novel method, TAbular Convolution (TAC), for classification of such data using CNNs by transforming tabular data to images and then classifying the images using CNNs. The transformation is performed by treating each row of tabular data (i.e., vector of features) as an image filter (kernel), and applying the filter to a fixed base image. A CNN is then trained to classify the filtered images. We applied TAC to classification of gene expression data derived from blood samples of patients with bacterial or viral infections. Our results demonstrate that off-the-shelf ResNet can classify the gene expression data as accurately as the current non-CNN state-of-the-art classifiers.

Competing Interest Statement

Ljubomir Buturovic is an employee of Inflammatix Inc.

Footnotes

  • dejanm{at}gmail.com

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted May 03, 2020.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A novel method for classification of tabular data using convolutional neural networks
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A novel method for classification of tabular data using convolutional neural networks
Ljubomir Buturović, Dejan Miljković
bioRxiv 2020.05.02.074203; doi: https://doi.org/10.1101/2020.05.02.074203
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A novel method for classification of tabular data using convolutional neural networks
Ljubomir Buturović, Dejan Miljković
bioRxiv 2020.05.02.074203; doi: https://doi.org/10.1101/2020.05.02.074203

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4117)
  • Biochemistry (8820)
  • Bioengineering (6523)
  • Bioinformatics (23470)
  • Biophysics (11798)
  • Cancer Biology (9216)
  • Cell Biology (13327)
  • Clinical Trials (138)
  • Developmental Biology (7440)
  • Ecology (11417)
  • Epidemiology (2066)
  • Evolutionary Biology (15160)
  • Genetics (10442)
  • Genomics (14051)
  • Immunology (9176)
  • Microbiology (22170)
  • Molecular Biology (8817)
  • Neuroscience (47600)
  • Paleontology (350)
  • Pathology (1429)
  • Pharmacology and Toxicology (2492)
  • Physiology (3733)
  • Plant Biology (8084)
  • Scientific Communication and Education (1437)
  • Synthetic Biology (2221)
  • Systems Biology (6039)
  • Zoology (1254)