GDNF (Glial cell line-derived neurotrophic factor) is a member of the transforming growth factor-beta superfamily, GDNF was was first recognized from glial cells for the ability to protect dopaminergic neurons in the midbrain (Engele et al., 1991; Lin et al., 1993), GDNF is also for ENS (Enteric Nervous System). At current it is known that overexpression, or abnormal expression of GDNF cause brain tumor, hirschsprung disease and especially related to neurocognitive functions (schizophrenia) and tendency of habit, for example GDNF has been regarded as potential common addiction-related gene (for instance, drug use, alcohol abuse, etc.). This is probably because GDNF is expressed in brain and other neuron cells.

When analyzing the GDNF interaction with other genes at https://genemania.org/, for instance with PTGS2 (or COX-2, an enzyme for prostaglandins): https://genemania.org/search/homo-sapiens/GDNF/PTGS2, then I got a quick idea, just wonder if these gene network could be converted to artificial intelligent neural network?

The primary idea is to make a model, or through some ready model and quickly convert the GDNF, or GDNF/PTGS2 gene network to a current widely recognized neural network, which may further develop into application (APP) for psychiatry or other clinic by analyzing certain target genes from blood samples of the individuals.

 RNN: Recurrent Neural Networks (RNNs) are a class of neural networks that help model sequence data. RNNs are derived from feedforward networks and exhibit behavior similar to how the human brain functions. In short: Recurrent Neural Networks produce predictions in sequential data that other algorithms cannot.

CNN: is a "feedforward neural network" that uses filter and pooling layers, while RNN feeds the results back into the network. In CNN, the size of the input and the resulting output are fixed.

Transformer: Models have been one of the main highlights of the advancement of deep learning and deep neural networks. Transformer neural networks are acyclic models for processing sequential data such as text.

Build neural networks in Python using packages like Keras and TensorFlow.

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