A DNA-derived phage nose using machine learning and artificial neural processing for diagnosing lung cancer

Abstract

There is a growing interest in electronic nose-based diagnostic systems that are fast and portable. However, existing technologies are suitable only for operation in the laboratory, making them difficult to apply in a rapid, non-face-to-face, and field-suitable manner. Here, we demonstrate a DNA-derived phage nose (D²pNose) as a portable respiratory disease diagnosis system requiring no pretreatment. D²pNose was produced based on phage colour films implanted with DNA sequences from mammalian olfactory receptor cells, and as a result, it possesses the comprehensive reactivity of these cells. The manipulated surface chemistry of the genetically engineered phages was verified through a correlation analysis between the calculated and the experimentally measured reactivity. Breaths from 31 healthy subjects and 31 lung cancer patients were collected and exposed to D²pNose without pretreatment. With the help of deep learning and neural pattern separation, D²pNose has achieved a diagnostic success rate of over 75% and a classification success rate of over 86% for lung cancer based on raw human breath. Based on these results, D²pNose can be expected to be directly applicable to other respiratory diseases.