Towards a robust out-of-the-box neural network model for genomic data

Abstract

Background 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. Results 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. Conclusions 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.

Non-linear phylogenetic regression using regularized kernels

“…The use of a regularization term in the error function enhances the model’s generalizability and robustness (Tibshirani, 1996; Zhang et al ., 2020; Jiang et al ., 2022), provided it is properly tuned (Srivastava et al ., 2014; Zhang et al ., 2021). This technique, which also assumes the independence of instances, has recently been proposed for PGLS (Adams et al ., 2022).…”