Artificial intelligence-enabled microbiome-based diagnosis models for a broad spectrum of cancer types

Microbiome-based diagnosis of cancer is an increasingly important supplement for the genomics approach in cancer diagnosis, yet current models for microbiome-based diagnosis of cancer face difficulties in generality: not only diagnosis models could not be adapted from one cancer to another, but models built based on microbes from tissues could not be adapted for diagnosis based on microbes from blood. Therefore, a microbiome-based model suitable for a broad spectrum of cancer types is urgently needed. Here we have introduced DeepMicroCancer, a diagnosis model using artificial intelligence techniques for a broad spectrum of cancer types. Built based on the random forest models it has enabled superior performances on more than twenty types of cancers' tissue samples. And by using the transfer learning techniques, improved accuracies could be obtained, especially for cancer types with only a few samples, which could satisfy the requirement in clinical scenarios. Moreover, transfer learning techniques have enabled high diagnosis accuracy that could also be achieved for blood samples. These results indicated that certain sets of microbes could, if excavated using advanced artificial techniques, reveal the intricate differences among cancers and healthy individuals. Collectively, DeepMicroCancer has provided a new venue for accurate diagnosis of cancer based on tissue and blood materials, which could potentially be used in clinics.

Automated summary

Microbiome-based diagnosis of cancer is a necessary addition to genomics approach, but current models face challenges in generalization to different cancer types and sample sources. DeepMicroCancer, an AI-based diagnosis model built on random forest models, shows superior performance on various cancer types' tissue samples. By utilizing transfer learning techniques, improved accuracy can be achieved, especially for cancer types with limited samples, meeting clinical requirements. The model also enables high diagnosis accuracy for blood samples. These results demonstrate the potential of using advanced artificial techniques to uncover complex differences between cancers and healthy individuals. DeepMicroCancer provides a new avenue for accurate cancer diagnosis based on tissue and blood materials in clinical settings.