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Detecting fabrication in large-scale molecular omics data

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  • Michael S Bradshaw
  • Samuel H Payne

Abstract

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. In the financial sector, machine learning and digit-frequencies 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 gene copy-number data as input, machine learning models correctly predicted fraud with 58–100% accuracy. With digit frequency as input features, the models detected fraud with 82%-100% accuracy. All of the data and analysis scripts used in this project are available at https://github.com/MSBradshaw/FakeData.

Suggested Citation

  • Michael S Bradshaw & Samuel H Payne, 2021. "Detecting fabrication in large-scale molecular omics data," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-15, November.
    • Handle: RePEc:plo:pone00:0260395
    DOI: 10.1371/journal.pone.0260395
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    2. Daniele Fanelli, 2009. "How Many Scientists Fabricate and Falsify Research? A Systematic Review and Meta-Analysis of Survey Data," PLOS ONE, Public Library of Science, vol. 4(5), pages 1-11, May.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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