Despite substantial standardization, polymerase chain reaction (PCR) experiments frequently fail. Troubleshooting failed PCRs can be costly in both time and money. Using a crowdsourced data set spanning 290 real PCRs from six active research laboratories, we investigate the degree to which PCR success rates can be improved by machine learning. While human designed PCRs succeed at a rate of 55-64%, we find that a machine learning model can accurately predict reaction outcome 81% of the time. We validate this level of improvement by then using the model to guide the design and predict the outcome of 39 new PCR experiments. In addition to improving outcomes, the model identifies 15 features of PCRs that researchers did not optimize well compared to the learned model. These results suggest that PCR success rates can easily be improved by 17-26%, potentially saving millions of dollars and thousands of hours of researcher time each year across the scientific community. Other common laboratory methods may benefit from similar data-driven optimization effort.

Breast cancer is a leading cause of cancer‐related deaths in women. Many genetic and behavioral risk factors can contribute to the initiation and progression of breast cancer, one being alcohol consumption. Numerous epidemiological studies have established a positive correlation between alcohol consumption and breast cancer; however, the molecular basis for this link remains ill defined. Elucidating ethanol‐induced changes to global transcriptional programming in breast cells is important to ultimately understand how alcohol and breast cancer are connected mechanistically. We investigated induced transcriptional changes in response to a short cellular exposure to moderate levels of alcohol. We treated the non‐tumorigenic breast cell line MCF10A, and the tumorigenic breast cell lines MDA‐MB‐231 and MCF7, with ethanol for 6 h, then captured the changes to ongoing transcription using 4‐thiouridine metabolic labeling followed by deep sequencing. Only the MCF10A cell line exhibited statistically significant changes in newly transcribed RNA in response to ethanol treatment. Further experiments revealed that some ethanol‐upregulated genes are sensitive to the dose of alcohol treatment, while others are not. Gene ontology and biochemical pathway analyses revealed that ethanol‐upregulated genes in MCF10A cells are enriched in biological functions that could contribute to cancer development.