PT  - JOURNAL ARTICLE
AU  - Michael S. Bradshaw
AU  - Samuel H. Payne
TI  - Detecting fabrication in large-scale molecular omics data
AID  - 10.1101/757070
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 757070
4099  - http://biorxiv.org/content/early/2020/10/21/757070.short
4100  - http://biorxiv.org/content/early/2020/10/21/757070.full
AB  - 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-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. All of the data and analysis scripts used in this project are available at https://github.com/MSBradshaw/FakeData.Competing Interest StatementThe authors have declared no competing interest.