Antibody Watch: Text mining antibody specificity from the literature

Chun-Nan Hsu, Chia-Hui Chang, Thamolwan Poopradubsil, Amanda Lo, Karen A. William, Ko-Wei Lin, Anita E. Bandrowski, Ibrahim Burak Özyurt, Jeffrey S. Grethe, Maryann E. Martone

Abstract: Author summary Antibodies are widely used reagents to test for the expression of proteins. However, antibodies are also a known source of reproducibility problems in biomedicine, as specificity and other issues can complicate their use. Information about how antibodies perform for specific applications are scattered across the biomedical literature and multiple websites. To alert scientists with reported antibody issues, we develop text mining algorithms that can identify specificity issues reported in the literature. We developed a deep neural network algorithm and performed a feasibility study on 2,223 papers. We leveraged Research Resource Identifiers (RRIDs), unique identifiers for antibodies and other biomedical resources, to match extracted specificity issues with particular antibodies. The results show that our system, called “Antibody Watch,” can accurately perform specificity issue identification and RRID association with a weighted F-score over 0.914. From our test corpus, we identified 37 antibodies with 68 nonspecific issue statements. With Antibody Watch, for example, if one were looking for an antibody targeting beta-Amyloid 1–16, from 74 antibodies at dkNET Resource Reports (on 10/2/20), one would be alerted that “some non-specific bands were detected at 55 kDa in both WT and APP/PS1 mice with the 6E10 antibody…”