Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach

Identifying unique exposure-specific transgenerational differentially DNA methylated region epimutations in the genome using hybrid deep learning prediction models

Exposure to environmental toxicants can lead to epimutations in the genome and an increase in differential DNA methylated regions (DMRs) that have been linked to increased susceptibility to various diseases. However, the unique effect of particular toxicants on the genome in terms of leading to unique DMRs for the toxicants has been less studied. One hurdle to such studies is the low number of observed DMRs per toxicants. To address this hurdle, a previously validated hybrid deep-learning cross-exposure prediction model is trained per exposure and used to predict exposure-specific DMRs in the genome. Given these predicted exposure-specific DMRs, a set of unique DMRs per exposure can be identified. Analysis of these unique DMRs through visualization, DNA sequence motif matching, and gene association reveals known and unknown links between individual exposures and their unique effects on the genome. The results indicate the potential ability to define exposure-specific epigenetic markers in the genome and the potential relative impact of different exposures. Therefore, a computational approach to predict exposure-specific transgenerational epimutations was developed, which supported the exposure specificity of ancestral toxicant actions and provided epigenome information on the DMR sites predicted.

Hybrid deep learning approach to improve classification of low-volume high-dimensional data

BACKGROUND

The performance of machine learning classification methods relies heavily on the choice of features. In many domains, feature generation can be labor-intensive and require domain knowledge, and feature selection methods do not scale well in high-dimensional datasets. Deep learning has shown success in feature generation but requires large datasets to achieve high classification accuracy. Biology domains typically exhibit these challenges with numerous handcrafted features (high-dimensional) and small amounts of training data (low volume).

METHOD

A hybrid learning approach is proposed that first trains a deep network on the training data, extracts features from the deep network, and then uses these features to re-express the data for input to a non-deep learning method, which is trained to perform the final classification.

RESULTS

The approach is systematically evaluated to determine the best layer of the deep learning network from which to extract features and the threshold on training data volume that prefers this approach. Results from several domains show that this hybrid approach outperforms standalone deep and non-deep learning methods, especially on low-volume, high-dimensional datasets. The diverse collection of datasets further supports the robustness of the approach across different domains.

CONCLUSIONS

The hybrid approach combines the strengths of deep and non-deep learning paradigms to achieve high performance on high-dimensional, low volume learning tasks that are typical in biology domains.