Deep neural networks (DNNs) have set benchmarks on a wide array of supervised learning
tasks. Trained DNNs, however, often lack robustness to minor adversarial perturbations to …

Current approaches to single-cell transcriptomic analysis are computationally intensive and
require assay-specific modeling, which limits their scope and generality. We propose a …

Single-cell computational pipelines involve two critical steps: organizing cells (clustering)
and identifying the markers driving this organization (differential expression analysis). State-of-…

Background With the recent proliferation of single-cell RNA-Seq experiments, several methods
have been developed for unsupervised analysis of the resulting datasets. These methods …

The success of deep neural networks stems from their ability to generalize well on real data;
however, et al. have observed that neural networks can easily overfit randomly-generated …

Single-cell technologies have seen widespread adoption in recent years. The datasets
generated by these technologies provide information on up to millions or more individual cells; …

We explore how deep recurrent neural network (RNN) architectures can be used to capture
the structure within a genetic sequence. We first confirm that a characterlevel RNN can …

Checkpoint blockade with antibodies specific for cytotoxic T lymphocyte–associated protein (CTLA)-4
or programmed cell death 1 (PDCD1; also known as PD-1) elicits durable tumor …

Background Mastering laparoscopic surgical skills requires considerable time and effort.
The Virtual Basic Laparoscopic Skill Trainer (VBLaST-PT©) is being developed as a …

Dispersion fuels represent a significant departure from typical ceramic fuels to address
swelling and radiation damage in high burnup fuel. Such fuels use a manufacturing process in …