Detecting small variations in phenotypes or class labels, such as those used in genome analysis, is an important but challenging task. Even with sufficient data, identifying genes or relevant features is not easy.
It is generally known that CNN is an effective method for image data, but it was harder to apply for genetic research that requires handling of non-image data such as RNA sequence data.
RIKEN is the largest research organization for basic and applied science in Japan. In their DeepInsight project, the researchers converted non-image data to image format to apply CNN effectively. Using MATLAB® to place similar elements together in a cluster, the team performed feature extraction of non-image data and identified hidden mechanisms.