We developed a protein-proximity assay in yeast based on fusing a histone lysine methyltransferase onto a bait and its substrate onto a prey. Upon binding, the prey is stably methylated and detected by methylation-specific antibodies. We applied this approach to detect varying interaction affinities among proteins in a mitogen-activated protein kinase pathway and to detect short-lived interactions between protein phosphatase 2A and its substrates that have so far escaped direct detection.

The aim of this study is to explore trends in retracted publications in life sciences and biomedical sciences over axes like time, countries, journals and impact factors, and topics. Nearly seven thousand publications, which comprise the entirety of retractions visible through PubMed as of August 2019, were used. This work involved sophisticated data collection and analysis techniques to use data from PubMed, Wikipedia, and WikiData, and study it with respect to the above mentioned axes. Importantly, I employ state-of-the-art analysis and visualization techniques from natural language processing (NLP) to understand the topics in retracted literature. To highlight a few results, the analyses demonstrate an increasing rate of retraction over time and noticeable differences in the publication quality (as measured by journal impact factor) among top countries. Moreover, while molecular biology and cancer dominate retractions, we also see a number of retractions not related to biology. The methods and results of this study can be applied to continuously understand the nature and evolution of retractions in life sciences, thus contributing to the health of this research ecosystem.

I explore trends in retracted publications in life sciences and biomedical sciences. Based on nearly seven thousand publications, which comprise the entirety of retractions visible through PubMed as of August 2019, I perform several analyses to understand trends over different axes, including time, countries, journals and impact factors, and topics. This work involved sophisticated data collection and analysis techniques to use data from PubMed, Wikipedia, and WikiData, and study the publications with respect to these axes. Importantly, I employ state-of-the-art analysis and visualization techniques from natural language processing (NLP) to understand the topics in retracted literature. To highlight a few results, the analyses demonstrate an increasing rate of retraction over time and noticeable differences in the publication quality (as measured by journal impact factors) among top publishing countries. Moreover, while molecular biology and cancer dominate retractions, we also see a number of retractions not related to biology. The methods and results of this study can be applied to continuously understand the nature and evolution of retractions in life sciences, thus contributing to the health of this research ecosystem.

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