Results 101 to 110 of about 5,846,406 (312)
Spatial biology in cancer epigenetics
Molecular Oncology, EarlyView.Spatial epigenomics combines molecular profiling with tissue architecture to reveal how gene regulation is organized within intact tissues. In cancer, these technologies uncover the mechanisms driving tumor heterogeneity and microenvironmental interactions, opening new opportunities for biomarker discovery and precision medicine.
Eva Crespo‐García, Manel Esteller
wiley +1 more source
Modeling Interestingness with Deep Neural Networks [PDF]
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014 An "Interestingness Modeler" uses deep neural networks to learn deep semantic models (DSM) of "interestingness." The DSM, consisting of two branches of deep neural networks or their convolutional versions, identifies and predicts target documents that would interest users reading source documents.
GAO JIANFENG +4 more
openaire +4 more sources
Molecular Oncology, EarlyView.Arginine methylation can be viewed as a persistence‐prone post‐translational modification regulated by a network of PRMTs. Competitive and compensatory interactions among PRMTs can redistribute methylation across substrate pools shaped by sequence, structural, spatial, and environmental layers, reinforcing RNA‐processing, chromatin, and signaling ...
So Hyun Kwon, Ji Min Lee
wiley +1 more source
Deep neural networks and humans both benefit from compositional language structure
Nature CommunicationsDeep neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to systematically produce forms for new meanings.
Lukas Galke, Yoav Ram, Limor Raviv
doaj +1 more source
Directed evolution of enzymes at the crossroads of tradition and innovation
FEBS Open Bio, EarlyView.An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
Guiding AlphaFold to predict how Munc13‐1 opens Syntaxin‐1
FEBS Open Bio, EarlyView.The syntaxin‐1 Habc‐domain (orange), linker (pink) and SNARE motif (yellow) form a closed conformation that binds to Munc18‐1 (violet) and is opened by the Munc13‐1 MUN domain (cyan) to form the SNARE complex that triggers neurotransmitter release.
Madhurima Chattopadhyay +2 more
wiley +1 more source
Symbolic manipulation based on deep neural networks and its application to axiom discovery
, 2017 Symbolic reasoning is difficult for neural networks. Especially, reasoning with variables can be a challenging task for them. In this paper, a symbolic reasoning method based on deep neural networks is proposed, and this method is applied to axiom ...
Yanyan Xu +7 more
core +1 more source
Algorithms, 2017 The literature [-5]contains several reports evaluating the abilities of deep neural networks in text transfer learning. To our knowledge, however, there have been few efforts to fully realize the potential of deep neural networks in cross-domain product ...
Xiaocong Wei +3 more
doaj +1 more source
FEBS Open Bio, EarlyView.TisIBP8, a fungal‐derived hyperactive ice‐binding protein, helps Caenorhabditis elegans survive dehydration. It localizes near cell membranes, reduces cell damage, and helps maintain membrane structure during drying. These results suggest that ice‐binding proteins can protect cells from dehydration stress as well as freezing stress.
Daiki Shimose +9 more
wiley +1 more source
Hierarchical Training of Deep Neural Networks Using Early Exiting [PDF]
Deep neural networks provide state-of-the-art accuracy for vision tasks but they require significant resources for training. Thus, they are trained on cloud servers far from the edge devices that acquire the data. This issue increases communication cost,
Pad, Pedram +5 more
core +1 more source