Results 111 to 120 of about 5,846,406 (312)

Integrating Deep Neural Networks and Symbolic Inference for Organic Reactivity Prediction [PDF]

, 2020
Accurate in silico models for the prediction of novel chemical reaction outcomes can be used to guide the rapid discovery of new reactivity and enable novel synthesis strategies for newly discovered lead compounds.
Wesley Wei, Qian, Nathan T., Russell, Claire L. W., Simons, Jian, Peng, Martin D., Burke, Yunan, Luo   +5 more
core   +1 more source

Training deep quantum neural networks

Nature Communications, 2020
It is hard to design quantum neural networks able to work with quantum data. Here, the authors propose a noise-robust architecture for a feedforward quantum neural network, with qudits as neurons and arbitrary unitary operations as perceptrons, whose ...
Kerstin Beer, Dmytro Bondarenko, Terry Farrelly, Tobias J. Osborne, Robert Salzmann, Daniel Scheiermann, Ramona Wolf   +6 more
doaj   +1 more source

Loss of AMBRA1 activates MAPK and angiogenesis signaling pathways in melanoma cells

FEBS Open Bio, EarlyView.
Loss of AMBRA1 in melanoma cells activates multiple oncogenic pathways associated with tumor progression. Transcriptomic and protein network analyses revealed that AMBRA1 depletion enhances MAPK/ERK signaling, angiogenesis, TGF‐β/EMT signaling, and Wnt/axon guidance pathways.
Milad Ibrahim, Marco Corazzari, Iman Osman, Jane Armstrong, Noel Carter   +4 more
wiley   +1 more source

Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications [PDF]

Deep neural networks for graphs (DNNGs) represent an emerging field that studies how the deep learning method can be generalized to graph-structured data.
Gnecco, Giorgio Stefano, Lio, P, Li, Ming, Lió, Pietro, Li, M, Sanguineti, M, Micheli, A, Pan, S, Wang, YG, Micheli, Alessio, Gnecco, GS, Wang, Yu Guang, Pan, Shirui, Sanguineti, Marcello   +13 more
core   +1 more source

Library Information Search in the Age of Digital Transformation: The Potential of Neural Networks and Generative Artificial Intelligence

Библиосфера
Today, as the information society evolves and libraries undergo digital transformation, there is a pressing need to find a balance between the vast volume of electronic information and the personalized needs of users.
D. N. Gribkov
doaj   +1 more source

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †

Energies, 2018
Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech ...
Gregory D. Merkel, Richard J. Povinelli, Ronald H. Brown   +2 more
doaj   +1 more source

From energy provision to protein synthesis: Tunnelling nanotubes as mediators of intercellular metabolic cooperation in cancer

FEBS Open Bio, EarlyView.
The cytoskeleton‐mediated transport of mitochondria via tunnelling nanotubes restores respiration, increases ATP production, rescues cells from apoptosis, activates the AKT/mTOR signalling pathway, promotes cell migration and invasiveness, contributes to cancer progression and treatment resistance.
Stanislava Martínková, Jan Trnka
wiley   +1 more source

Adenosine triphosphate as a modulator of protein interactions and stability

FEBS Open Bio, EarlyView.
ATP is best known as the cell's energy currency, but it also shapes how proteins fold, interact, aggregate and form biomolecular condensates. This review explains the emerging physical principles behind these effects, including weak binding to charged protein regions, magnesium‐dependent behaviour and concentration‐dependent control of protein ...
Shuyuan Tan, Robin Curtis
wiley   +1 more source

Dynamic neuron approach to deep neural networks: Decoupling neurons for renormalization group analysis

Physical Review Research
Deep neural network architectures often consist of repetitive structural elements. We introduce an approach that reveals these patterns and can be broadly applied to the study of deep learning.
Donghee Lee, Hye-Sung Lee, Jaeok Yi
doaj   +1 more source

Chronobiology of Cancer: How Aging Fuels Oncogenesis at the Molecular Level

Aging and Cancer, EarlyView.
This graphical abstract illustrates the key biological pathways linking aging with cancer development and progression. In the upper left, cumulative exposure to ultraviolet radiation, toxins, and reactive oxygen species (ROS) causes DNA damage and genomic instability, whereas age‐related decline in repair mechanisms, such as ATM/ATR, BER, and NER ...
Anu Singh, Aroonima Misra, Sufian Zaheer
wiley   +1 more source