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Interaction Prediction

Facebook has developed an artificial intelligence approach that it says can quickly predict how drugs will interact within cells and help identify combinations of drugs that could be used to treat cancer, New Scientist reports.

As researchers led by Facebook's David Lopez-Paz report in a preprint posted to BioRxiv, they developed an approach dubbed Compositional Perturbation Autoencode (CPA) to model and predict single-cell changes that occur with exposure to different drugs and doses, across cell types. According to New Scientist, the researchers found that their CPA approach could predict cell responses with about 90 percent accuracy and say this approach could hasten researchers' abilities to develop new treatments, including for cancer.

The US National Cancer Institute's Eytan Ruppin, who was not part of the study, tells it that the study is "important" first step, but that more testing is needed, especially as CPA predicts RNA changes that occur in the cell following treatment, but not whether that treatment leads the cell to die. "We have cured cancer one hundred times in salines and mouse models. They have shown nothing at all that is relevant to patients," he adds at New Scientist.

The Scan

Study Examines Insights Gained by Adjunct Trio RNA Sequencing in Complex Pediatric Disease Cases

Researchers in AJHG explore the diagnostic utility of adding parent-child RNA-seq to genome sequencing in dozens of families with complex, undiagnosed genetic disease.

Clinical Genomic Lab Survey Looks at Workforce Needs

Investigators use a survey approach in Genetics in Medicine Open to assess technologist applications, retention, and workforce gaps at molecular genetics and clinical cytogenetics labs in the US.

Study Considers Gene Regulatory Features Available by Sequence-Based Modeling

Investigators in Genome Biology set sequence-based models against observational and perturbation assay data, finding distal enhancer models lag behind promoter predictions.

Genetic Testing Approach Explores Origins of Blastocyst Aneuploidy

Investigators in AJHG distinguish between aneuploidy events related to meiotic missegregation in haploid cells and those involving post-zygotic mitotic errors and mosaicism.