Lost or misattributed works by some of the finest writers of Spain's Golden Age could be discovered thanks to pioneering AI technology that has been used to identify a previously unknown play by the wildly prolific dramatist, poet, sailor and priest Lope de Vega. This week Spain's National Library announced that researchers trawling its massive archive had stumbled upon and verified a play that Lope is believed to have written a few years before his death in 1635. Like many plays of the Spanish Golden Age – the 16th- and 17th-century cultural boom that accompanied Spain's imperial growth and which birthed masterpieces by Lope, Cervantes, Calderón and Velázquez, among many others – La francesa Laura (The Frenchwoman Laura) is a tale of love, jealousy and social hierarchy in which suspicion demands an innocent woman be sacrificed on the altar of her husband's honour. But, unlike many similar plays of the period, Laura survives and the third act ends happily. Equally unusual was the manner of the play's discovery.

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage responses, some genomic lesions trigger malignant transformation of cells. Accurate prediction of carcinogens is an ever-challenging task owing to the limited information about bona fide (non-)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable and outperforms existing best-practice methods for carcinogenicity prediction. Metabokiller unraveled potential carcinogenic human metabolites. To cross-validate Metabokiller predictions, we performed multiple functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites, namely 4-nitrocatechol and 3,4-dihydroxyphenylacetic acid, and observed high synergy between Metabokiller predictions and experimental validations. Metabokiller is a novel, explainable AI-backed method for carcinogenicity prediction that leverages the biological and chemical properties associated with carcinogens.

Machine learning can pinpoint "genes of importance" that help crops to grow with less fertilizer, according to a new study published in Nature Communications. It can also predict additional traits in plants and disease outcomes in animals, illustrating its applications beyond agriculture. Using genomic data to predict outcomes in agriculture and medicine is both a promise and challenge for systems biology. Researchers have been working to determine how to best use the vast amount of genomic data available to predict how organisms respond to changes in nutrition, toxins, and pathogen exposure--which in turn would inform crop improvement, disease prognosis, epidemiology, and public health. However, accurately predicting such complex outcomes in agriculture and medicine from genome-scale information remains a significant challenge.

These are among the expenses employees bill to their companies, according to AppZen, a provider of financial software. You've got to hand it to them, employees are certainly creative in their perceptions of what constitutes business expenses. Businesses process an average of 100,000 expense reports and 700,000 invoices a year, and that means suspect expenses are falling through the cracks, according to AppZen's The State of Business Spend report for Q4 2019. The problem is that most modern invoice automation systems can look out for two invoices with the same invoice number, or the same amount, and stop a payment that appears to be a duplicate, according to AppZen. But they don't find typos in invoice numbers, duplicates across expense and AP systems – or, ahem, interesting items employees bill to their employers.