NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern

TL;DR: A multimodal gait-abnormality-recognition framework based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network is proposed, which is more suitable for gait monitoring in daily life and the customization of medical rehabilitation schedules, which will help more patients alleviate the harm caused by their diseases.

TL;DR: This study investigates the simultaneous time-frequency analysis of gait signals from both legs to classify neurodegenerative diseases, achieving high accuracy (up to 99.20%) using deep transfer learning models and wavelet coherence analysis.

TL;DR: In this article , the authors propose a novel framework for gait data augmentation by using OpenSIM, a physics-based simulator, to synthesize biomechanically plausible walking sequences.

TL;DR: This study applies explainable deep learning methodologies to single-cell RNA sequencing data to elucidate the biological underpinnings of Huntington's disease, focusing on the relationship between mRNA expression and disease pathology at the cellular level.

TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.

TL;DR: This paper designs a novel type of neural network that directly consumes point clouds, which well respects the permutation invariance of points in the input and provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing.

TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).

TL;DR: The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.