Table 1.

Common architectures of artificial neural networks. The topology of an artificial neural network has strong influence on the performance of the model. Different architectures are more appropriate for certain data types

Architecture	Description
Fully connected neural networks	FCNNs are the most conventional deep neural networks (DNNs).
	In a layer, each neuron is connected to all neurons in the
	subsequent layers [12].
Convolutional neural networks	CNNs are able to model spatial structures such as images
	or DNA sequences. Each neuron is connected to all neurons
	in the subsequent layer. In convolution layers kernels are
	slide over the input data to model local information [12].
Recurrent neural networks	RNNs model sequential data well by maintaining a state
	vector that encodes the information of previous time steps.
	This state is represented by the hidden units of the network
	and is updated at each time step [12].
Graph neural networks	GNNs model graphs consisting of entities and their connec-
	tions representing e.g. molecules or nuclei of a tissue. Layers
	of GNNs can take on different forms such as convolutions and
	recurrence [14].
Autoencoders	AEs learn a lower dimensional encoding of the input data by
	first compressing it and then reconstructing the original input
	data. Layers can be of different types such as fully connected
	or convolutional [15].