Abstract: A computer-implemented method for inferring a 3D structure of a genome is provided. The method includes providing genome interaction data and operating an autoencoder including a structured sequence of n autoencoder units, each of which including an encoder unit and a decoder unit, each of which is implemented as a recurrent neural network unit. The method includes additionally training the autoencoder by feeding all vectors of genome interaction data to the encoder units. Thereby, the training of the auto-encoder units is performed stepwise by using inner state of respective previous autoencoder units in the cascaded sequence of autoencoder units and performing backpropagation within each of the plurality of autoencoder units after all autoencoder units have processed their respective input values, and using the output values of the encoder units for deriving a 3D model for a visualization of the genome.

Abstract: A computer-implemented method for inferring a 3D structure of a genome is provided. The method includes providing genome interaction data and operating an autoencoder including a structured sequence of n autoencoder units, each of which including an encoder unit and a decoder unit, each of which is implemented as a recurrent neural network unit. The method includes additionally training the autoencoder by feeding all vectors of genome interaction data to the encoder units. Thereby, the training of the auto-encoder units is performed stepwise by using inner state of respective previous autoencoder units in the cascaded sequence of autoencoder units and performing backpropagation within each of the plurality of autoencoder units after all autoencoder units have processed their respective input values, and using the output values of the encoder units for deriving a 3D model for a visualization of the genome.