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

Detecting fabrication in large-scale molecular omics data

Michael S. Bradshaw, View ORCID ProfileSamuel H. Payne
doi: https://doi.org/10.1101/757070
Michael S. Bradshaw
Biology Department, Brigham Young University, Provo UT 84602 USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Samuel H. Payne
Biology Department, Brigham Young University, Provo UT 84602 USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Samuel H. Payne
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Motivation Fraud is a pervasive problem and can occur as fabrication, falsification, plagiarism or theft. The scientific community is not exempt from this universal problem and several studies have recently been caught manipulating or fabricating data. Current measures to prevent and deter scientific misconduct come in the form of the peer-review process and on-site clinical trial auditors. As recent advances in high-throughput omics technologies have moved biology into the realm of big-data, fraud detection methods must be updated for sophisticated computational fraud.

Results In the financial sector, machine learning and digit-preference are successfully used to detect fraud. Drawing from these sources, we develop methods of fabrication detection in biomedical research and show that machine learning can be used to detect fraud in large-scale omic experiments. Using the raw data as input, the best machine learning models correctly predicted fraud with 84-95% accuracy. With digit frequency as input features, the best models detected fraud with 98%-100% accuracy.

Availability and Implementation All of the data and analysis scripts used in this project are available at https://github.com/MSBradshaw/Holden.

Contact sam_payne{at}byu.edu

Supplemental Information Supplemental figures accompany the manuscript online

Footnotes

  • https://github.com/MSBradshaw/Holden

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 September 05, 2019.
Download PDF
Data/Code
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.
Detecting fabrication in large-scale molecular omics data
(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
Detecting fabrication in large-scale molecular omics data
Michael S. Bradshaw, Samuel H. Payne
bioRxiv 757070; doi: https://doi.org/10.1101/757070
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Detecting fabrication in large-scale molecular omics data
Michael S. Bradshaw, Samuel H. Payne
bioRxiv 757070; doi: https://doi.org/10.1101/757070

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 (4864)
  • Biochemistry (10821)
  • Bioengineering (8061)
  • Bioinformatics (27370)
  • Biophysics (14014)
  • Cancer Biology (11157)
  • Cell Biology (16094)
  • Clinical Trials (138)
  • Developmental Biology (8806)
  • Ecology (13323)
  • Epidemiology (2067)
  • Evolutionary Biology (17390)
  • Genetics (11704)
  • Genomics (15957)
  • Immunology (11057)
  • Microbiology (26148)
  • Molecular Biology (10674)
  • Neuroscience (56714)
  • Paleontology (422)
  • Pathology (1737)
  • Pharmacology and Toxicology (3012)
  • Physiology (4566)
  • Plant Biology (9662)
  • Scientific Communication and Education (1617)
  • Synthetic Biology (2697)
  • Systems Biology (6993)
  • Zoology (1513)