This study revolves around the protein arginine methyltransferase PRMT9, revealing its important role in acute myeloid leukemia (AML) and its potential as an anticancer target. Researchers found that PRMT9 expression levels were significantly elevated in AML stem cells and leukemic cells. Through gene editing and chemical probes, they discovered th...

The 5’ untranslated region (5’UTR) is a regulatory region at the start of messenger RNA (mRNA) molecules, playing a crucial role in regulating the translation process and affecting protein expression levels. Language models have demonstrated effectiveness in decoding protein and genomic sequence functions. In this study, the authors introduce a lan...

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...

Currently, there are significant challenges in studying the statistical and geometrical properties of particles carried by the fluid in turbulence. Despite outstanding efforts in theory, numerical simulations, and experiments over the past 30 years, there is still a lack of models that can realistically reproduce the statistical and topological fea...

This research proposes an online learning method for efficiently constructing surrogate models that can accurately emulate complex systems. The method consists of three key components: Sampling strategy for generating new training and testing data; Learning strategy for generating candidate surrogate models based on the training data; Validation me...

In this study, researchers explored the role of the TAK1 activation pathway in CAR T cell therapy-associated immune effector cell-associated neurotoxicity syndrome (ICANS). They established a mouse ICANS model and found that following the transfer of CAR19 T cells, cerebellar cells were activated, underwent morphological changes, and expressed more...