Co-Inference of Data Mislabelings Reveals Improved Models in Genomics and Breast Cancer Diagnostics - Gerber, Susanne; Pospisil, Lukas; Sys, Stanislav; Hewel, Charlotte; Torkamani, Ali; Horenko, Illia (Frontiers in Artificial Intelligence, 2022)
Mislabeling of cases as well as controls in case-control studies is a frequent source of strong bias in prognostic and diagnostic tests and algorithms. Common data processing methods available to the researchers in the biomedical community do not allow for consistent and robust treatment of labeled data in situations where both the case and the control groups contain a non-negligible proportion of mislabeled data instances. This is an especially prominent issue in studies regarding late-onset conditions, where individuals who may convert to cases may populate the control group, and for screening studies that often have high false positive/negative rates. To address this problem, we propose a method for simultaneous robust inference of Lasso-reduced discriminative models and of latent group-specific mislabeling risks, not requiring any exactly-labeled data. We apply it to a standard breast cancer imaging data set and infer the mislabelling probabilities (being rates of false-negative and false-positive core needle biopsies) together with a small set of simple diagnostic rules, outperforming the state-of-the-art BI-RADS diagnostics on these data. The inferred mislabeling rates for breast cancer biopsies agree with the published purely empirical studies. Applying the method to human genomic data from a healthy-aging cohort reveals a previously unreported compact combination of Single Nucleotide Polymorphisms that are strongly associated with a healthy-aging phenotype for Caucasians. It determines that 7.5% of Caucasians in the 1000-genomes dataset (selected as a control group) carry this pattern characteristic for healthy aging.