Differentiating Visual and Semantic Information in Human Perception and Memory Using Deep Neural Networks Introduction In cognitive science, the study of how humans recognize individuals and objects during perception and memory processes has long been of interest. Successful recognition of people and objects relies on matching representations gener...

Background Introduction Cancer of Unknown Primary (CUP) is a type of malignant disease that is confirmed to be metastatic through histopathology but whose primary site cannot be identified using conventional baseline diagnostic methods. CUP presents significant diagnostic and therapeutic challenges in clinical practice and is believed to account fo...

Using Medical Records to Predict Common Disease Incidence and Support Rapid Response to Emerging Health Threats Research Background and Motivation The COVID-19 pandemic exposed systemic issues and a lack of data-driven guidance globally, significantly affecting the identification of high-risk populations and pandemic preparedness. Assessing individ...

Dimond: A Study on Optimizing Diffusion Models through Deep Learning Academic Background In brain science and clinical applications, Diffusion Magnetic Resonance Imaging (dMRI) is an essential tool for non-invasively mapping the microstructure and neural connectivity of brain tissue. However, accurately estimating parameters of the diffusion signal...

DeepDTI: High-Fidelity Six-Direction Diffusion Tensor Imaging Using Deep Learning Research Background and Motivation Diffusion Tensor Imaging (DTI) boasts unparalleled advantages in mapping the microstructure and structural connectivity of live human brain tissue. However, traditional DTI techniques require extensive angular sampling, leading to pr...

This study utilized a machine learning framework to explore the underlying cell states and cellular ecosystems constituting soft tissue sarcomas, and associated them with patient prognosis and response to immunotherapy. Research Background: Soft tissue sarcomas are rare and highly heterogeneous malignancies of connective tissues, with limited syste...

Geometric Enhanced Pretraining for Interatomic Potentials Introduction Molecular dynamics (MD) simulations play an important role in fields such as physics, chemistry, biology, and materials science, providing insights into atomic-level processes. The accuracy and efficiency of MD simulations depend on the choice of interatomic potential functions ...

Major Breakthrough in Neuroscience Research: Deep Learning Technique Achieves Decoding of Natural Speech from Brain Signals A cross-disciplinary research team at New York University recently achieved a major breakthrough in the fields of neuroscience and artificial intelligence. They developed a novel deep learning-based framework that can directly...

From early drug discovery researchers face a daunting challenge – to find drug-like candidate molecules among approximately 10^60 possible molecular structures. One successful solution is to start from smaller “fragment” molecules, a strategy known as fragment-based drug design (FBDD). In the FBDD process, the first step is to computationally scree...

This is a paper about MassFormer, a graph transformer model for small molecule mass spectrometry prediction. This research addresses the problem of molecular identification in mass spectrometry data and proposes a novel deep learning approach to predict mass spectra of small molecules. Background: Mass spectrometry (MS) is an analytical technique w...