DeepCNV: a deep learning approach for authenticating copy number variations.

Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably high false positive rate. A common practice is to have human experts manually review original CNV calls for filtering false positives before further downstream analysis or experimental validation. Here, we propose DeepCNV, a deep learning-based tool, intended to replace human experts when validating CNV calls, focusing on the calls made by one of the most accurate CNV callers, PennCNV. The sophistication of the deep neural network algorithm is enriched with over 10 000 expert-scored samples that are split into training and testing sets. Variant confidence, especially for CNVs, is a main roadblock impeding the progress of linking CNVs with the disease. We show that DeepCNV adds to the confidence of the CNV calls with an optimal area under the receiver operating characteristic curve of 0.909, exceeding other machine learning methods. The superiority of DeepCNV was also benchmarked and confirmed using an experimental wet-lab validation dataset. We conclude that the improvement obtained by DeepCNV results in significantly fewer false positive results and failures to replicate the CNV association results.

A Deep Learning Approach to Competing Risks Representation in Peer-to-Peer Lending.

Online peer-to-peer (P2P) lending is expected to benefit both investors and borrowers due to their low transaction cost and the elimination of expensive intermediaries. From the lenders' perspective, maximizing their return on investment is an ultimate goal during their decision-making procedure. In this paper, we explore and address a fundamental problem underlying such a goal: how to represent the two competing risks, charge-off and prepayment, in funded loans. We propose to model both potential risks simultaneously, which remains largely unexplored until now. We first develop a hierarchical grading framework to integrate two risks of loans both qualitatively and quantitatively. Afterward, we introduce an end-to-end deep learning approach to solve this problem by breaking it down into multiple binary classification subproblems that are amenable to both feature representation and risks learning. Particularly, we leverage deep neural networks to jointly solve these subtasks, which leads to the in-depth exploration of the interaction involved in these tasks. To the best of our knowledge, this is the first attempt to characterize competing risks for loans in P2P lending via deep neural networks. The comprehensive experiments on real-world loan data show that our methodology is able to achieve an appealing investment performance by modeling the competition within and between risks explicitly and properly. The feature analysis based on saliency maps provides useful insights into payment dynamics of loans for potential investors intuitively.