Results 101 to 110 of about 9,085,112 (391)
Nature Communications, 2023 Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining spatial context. Here the authors present GraphST, a graph self-supervised contrastive learning method that learns informative and ...
Yahui Long +15 more
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Energy efficiency and coding of neural network
Frontiers in Neuroscience, 2023 Based on the Hodgkin-Huxley model, this study explored the energy efficiency of BA network, ER network, WS network, and Caenorhabditis elegans neural network, and explained the development of neural network structure in the brain from the perspective of ...
Shengnan Li, Chuankui Yan, Ying Liu
doaj +1 more source
Assessing Intelligence in Artificial Neural Networks [PDF]
arXiv, 2020 The purpose of this work was to develop of metrics to assess network architectures that balance neural network size and task performance. To this end, the concept of neural efficiency is introduced to measure neural layer utilization, and a second metric called artificial intelligence quotient (aIQ) was created to balance neural network performance and
arxiv
Mathematical Neural Networks [PDF]
Axioms, 2022 ANNs succeed in several tasks for real scenarios due to their high learning abilities. This paper focuses on theoretical aspects of ANNs to enhance the capacity of implementing those modifications that make ANNs absorb the defining features of each scenario.
openaire +3 more sources
Ubiquitination of transcription factors in cancer: unveiling therapeutic potential
Molecular Oncology, EarlyView.In cancer, dysregulated ubiquitination of transcription factors contributes to the uncontrolled growth and survival characteristics of tumors. Tumor suppressors are degraded by aberrant ubiquitination, or oncogenic transcription factors gain stability through ubiquitination, thereby promoting tumorigenesis.
Dongha Kim, Hye Jin Nam, Sung Hee Baek
wiley +1 more source
FEBS Open Bio, EarlyView.This review highlights how foundation models enhance predictive healthcare by integrating advanced digital twin modeling with multiomics and biomedical data. This approach supports disease management, risk assessment, and personalized medicine, with the goal of optimizing health outcomes through adaptive, interpretable digital simulations, accessible ...
Sakhaa Alsaedi +2 more
wiley +1 more source
Asymptotic Theory of Expectile Neural Networks [PDF]
arXiv, 2020 Neural networks are becoming an increasingly important tool in applications. However, neural networks are not widely used in statistical genetics. In this paper, we propose a new neural networks method called expectile neural networks. When the size of parameter is too large, the standard maximum likelihood procedures may not work.
arxiv
Journal of Computer and System Sciences, 2001 28 pages, 11 figures, To appear in Journal of Computer and System ...
Sanjay Gupta, R. K. P. Zia
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Aging and Cancer, EarlyView.Aging weakens the blood–brain barrier (BBB), increasing susceptibility to CNS cancers and complicating treatment. This review examines BBB deterioration, its impact on drug delivery, and potential interventions like targeting neuroinflammation and advanced therapies.
Quang La, Aiman Baloch, David F. Lo
wiley +1 more source
IEEE Access, 2018 The patient with ischemic stroke can benefit most from the earliest possible definitive diagnosis. While a quantitative evaluation of the stroke lesions on the magnetic resonance images (MRIs) is effective in clinical diagnosis, manually segmenting the ...
Zhiyang Liu +5 more
doaj +1 more source