LogGENE: A smooth alternative to check loss for Deep Healthcare Inference Tasks

TL;DR

LogGENE introduces a smooth, differentiable alternative to check loss for deep neural network inference in healthcare, enabling accurate, interpretable, and uncertainty-aware predictions in genomics and other medical datasets.

Contribution

The paper proposes LogGENE, a smooth approximation to check loss using log-cosh, improving training convergence and enabling uncertainty estimation in deep healthcare inference tasks.

Findings

Achieves state-of-the-art accuracy in gene expression prediction.

Provides interpretable conditional quantiles with uncertainty estimates.

Demonstrates faster convergence due to smooth loss function.

Abstract

Mining large datasets and obtaining calibrated predictions from tem is of immediate relevance and utility in reliable deep learning. In our work, we develop methods for Deep neural networks based inferences in such datasets like the Gene Expression. However, unlike typical Deep learning methods, our inferential technique, while achieving state-of-the-art performance in terms of accuracy, can also provide explanations, and report uncertainty estimates. We adopt the Quantile Regression framework to predict full conditional quantiles for a given set of housekeeping gene expressions. Conditional quantiles, in addition to being useful in providing rich interpretations of the predictions, are also robust to measurement noise. Our technique is particularly consequential in High-throughput Genomics, an area which is ushering a new era in personalized health care, and targeted drug design and delivery. However, check loss, used in quantile regression to drive the estimation process is not differentiable. We propose log-cosh as a smooth-alternative to the check loss. We apply our methods on GEO microarray dataset. We also extend the method to binary classification setting. Furthermore, we investigate other consequences of the smoothness of the loss in faster convergence. We further apply the classification framework to other healthcare inference tasks such as heart disease, breast cancer, diabetes etc. As a test of generalization ability of our framework, other non-healthcare related data sets for regression and classification tasks are also evaluated.