Cellular and biomolecular images contain a wealth of information that cannot be recognized from genetic or amino acid sequences alone. To harness this information, bioimage informatics technologies that extract biologically meaningful factors from image data using AI and machine learning are gaining attention. By employing machine learning and deep learning to automatically analyze and classify the vast data embedded in images, these technologies enable numerical analysis and predictive modeling. Furthermore, integrating and correlating image data with genetic and amino acid sequence information allows for more precise bioinformatics analyses.
In our laboratory, we are applying image informatics technologies to biosensing, focusing on the integrated analysis of image data such as microbial colony images and fluorescence images from DNA microarrays using machine learning. These efforts aim to develop technologies for simple and rapid microbial detection and screening, as well as early cancer diagnosis.