Results 71 to 80 of about 134,150 (259)

Physics-informed machine learning for automatic model reduction in chemical reaction networks

Scientific Reports
Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this synergy proves valuable, addressing the high computational
Joseph Pateras, Colin Zhang, Shriya Majumdar, Ayush Pal, Preetam Ghosh   +4 more
doaj   +1 more source

An unexpected alternative viologen electron mediator site in tungsten‐containing formate dehydrogenase

FEBS Letters, EarlyView.
An unexpected alternative interaction site for ethyl viologen was identified in formate dehydrogenase 1 from Methylorubrum extorquens. Combined mutagenesis, kinetic analysis, and docking revealed that aromatic residues near an iron–sulfur cluster enable flavin mononucleotide‐independent electron transfer, offering a framework for engineering improved ...
Eleni G. Poloniataki, Yong Hwan Kim
wiley   +1 more source

Physics-constrained machine learning for reduced composition space chemical kinetics

Data-Centric Engineering
Modeling detailed chemical kinetics is a primary challenge in combustion simulations. We present a novel framework to enforce physical constraints, specifically total mass and elemental conservation, during the reaction of ML models’ training for the ...
Anuj Kumar, Tarek Echekki
doaj   +1 more source

Application of Artificial Neural Networks in an Experimental Batch Reactor of Styrene Polymerization for Predictive Model Development

Chemical Engineering Transactions, 2013
Batch reactors are widely used in the polymer industry, especially for multi-purpose processes where different types of polymers are produced on demand.
R. Ribeiro Santos, B. Santos, A.M. Fileti, F.V. Silva, R. Zemp   +4 more
doaj   +1 more source

Basal State Calibration of a Chemical Reaction Network Model for Autophagy

International Journal of Molecular Sciences
The modulation of autophagy plays a dual role in tumor cells, with the potential to both promote and suppress tumor proliferation. In order to gain a deeper understanding of the nature of autophagy, we have developed a chemical reaction kinetic model of autophagy and apoptosis based on the mass action kinetic models that have been previously described ...
Bence Hajdú, Orsolya Kapuy, Tibor Nagy
openaire   +2 more sources

Tumour–host interactions in Drosophila: mechanisms in the tumour micro‐ and macroenvironment

Molecular Oncology, EarlyView.
This review examines how tumour–host crosstalk takes place at multiple levels of biological organisation, from local cell competition and immune crosstalk to organism‐wide metabolic and physiological collapse. Here, we integrate findings from Drosophila melanogaster studies that reveal conserved mechanisms through which tumours hijack host systems to ...
José Teles‐Reis, Tor Erik Rusten
wiley   +1 more source

Port-Hamiltonian modeling of non-isothermal chemical reaction networks [PDF]

Journal of Mathematical Chemistry, 2018
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Li, Maschke, Bernhard, Schaft, Arjan   +2 more
openaire   +4 more sources

Subtype‐specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Molecular Oncology, EarlyView.
This study employed machine learning methodologies to perform the subtype‐specific classification of RNA‐seq data sets, which are mapped on enhancers from TCGA‐derived breast cancer patients. Their integration with gene expression (referred to as ProxCReAM eRNAs) and chromatin accessibility profiles has the potential to identify lineage‐specific and ...
Aamena Y. Patel, Peyman Zarrineh, Nathnael T. Tuffa, Jigar H. Sheth, Sumitra Mohan, Mudassar Iqbal, Sankari Nagarajan   +6 more
wiley   +1 more source

Extreme learning with chemical reaction optimization for stock volatility prediction

Financial Innovation, 2020
Extreme learning machine (ELM) allows for fast learning and better generalization performance than conventional gradient-based learning. However, the possible inclusion of non-optimal weight and bias due to random selection and the need for more hidden ...
Sarat Chandra Nayak, Bijan Bihari Misra
doaj   +1 more source

EDNRB‐dependent endothelin signaling reduces proliferation and promotes proneural‐to‐mesenchymal transition in gliomas

Molecular Oncology, EarlyView.
Glioma cells mainly express the endothelin receptor EDNRB, while EDNRA is restricted to a perivascular tumor subpopulation. Endothelin signaling reduces glioma cell proliferation while promoting migration and a proneural‐to‐mesenchymal transition associated with poor prognosis. This pathway activates Ca2+, K+, ERK, and STAT3 signalings and is regulated
Donovan Pineau, Leonor Garcia, Hugo Arnold, Antonija Hanžek, Maialen Arrieta, Laurent R Gauthier, Christine Granotier‐Beckers, François D Boussin, Amaury Herbet, Marie Hautière, Valentin Asei‐Ceschino, Clémentin Jacques, Laura Brard, Thomas Harnois, Valérie Coronas, Bruno Constantin, Aurélien Chatelier, Jean Chemin, Serge Urbach, Martial Seveno, Szimonetta Hideg, Chantal Ripoll, Kasandra Aguilar‐Cázarez, Min Zheng, Guo‐Hao Huang, Sheng‐Qing Lv, Lei Zhang, Philippe Rondard, Laurent Prezeau, Jean‐Philippe Pin, Hugues Duffau, Luc Bauchet, Valérie Rigau, Franck Denat, Charles Truillet, Didier Boquet, Jean‐Philippe Hugnot   +36 more
wiley   +1 more source