  The accurate prediction of biological features from genomic data is paramount
for precision medicine and sustainable agriculture. For decades, neural network
models have been widely popular in fields like computer vision, astrophysics
and targeted marketing given their prediction accuracy and their robust
performance under big data settings. Yet neural network models have not made a
successful transition into the medical and biological world due to the
ubiquitous characteristics of biological data such as modest sample sizes,
sparsity, and extreme heterogeneity. Here, we investigate the robustness,
generalization potential and prediction accuracy of widely used convolutional
neural network and natural language processing models with a variety of
heterogeneous genomic datasets. Mainly, recurrent neural network models
outperform convolutional neural network models in terms of prediction accuracy,
overfitting and transferability across the datasets under study. While the
perspective of a robust out-of-the-box neural network model is out of reach, we
identify certain model characteristics that translate well across datasets and
could serve as a baseline model for translational researchers.
