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.