Optimizing polymerase chain reaction (PCR) using machine learning
Optimizing polymerase chain reaction (PCR) using machine learning
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 55–63%, 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.
Kavran Andrew J.、Cordaro Nicholas J.、Brant Tyler S.、DuMont Vanessa、Garcia Naiara Doherty、Smallegan Michael、Palacio Megan、Sawyer Sara L.、Lammer Nickolaus、Clauset Aaron、Miller Suzannah、Jourabchi Tara
Department of Biochemistry, University of ColoradoDepartment of Biochemistry, University of Colorado||Department of Computer Science, University of ColoradoDepartment of Biochemistry, University of ColoradoDepartment of Molecular Cellular and Developmental Biology, University of ColoradoDepartment of Biochemistry, University of ColoradoDepartment of Molecular Cellular and Developmental Biology, University of ColoradoDepartment of Biochemistry, University of ColoradoDepartment of Molecular Cellular and Developmental Biology, University of Colorado||BioFrontiers Institute, University of ColoradoDepartment of Biochemistry, University of ColoradoDepartment of Computer Science, University of Colorado||BioFrontiers Institute, University of Colorado||Santa Fe InstituteDepartment of Biochemistry, University of ColoradoDepartment of Biochemistry, University of Colorado
生物科学研究方法、生物科学研究技术计算技术、计算机技术分子生物学
Kavran Andrew J.,Cordaro Nicholas J.,Brant Tyler S.,DuMont Vanessa,Garcia Naiara Doherty,Smallegan Michael,Palacio Megan,Sawyer Sara L.,Lammer Nickolaus,Clauset Aaron,Miller Suzannah,Jourabchi Tara.Optimizing polymerase chain reaction (PCR) using machine learning[EB/OL].(2025-03-28)[2025-08-10].https://www.biorxiv.org/content/10.1101/2021.08.12.455589.点此复制
评论