Accurate RNA 3D structure prediction using a language model-based deep learning approach RhoFold+ is an end-to-end language model-based deep learning method to predict RNA three-dimensional structures of single-chain RNAs from sequences.
What is machine learning failure predictive maintenance? Predictive maintenance’s main goals How machine failure prediction improves systems Application of machine learning failure prediction in industries Predicting equipment failure using a machine learning algorithm The most used predictive modeling ...
EpiVerse is a deep-learning framework that integrates imputed epigenetic signals to improve cross-cell-type Hi-C prediction, enhance interpretability, and enable in silico perturbation of chromatin architecture. Ming-Yu Lin ,Yu-Cheng Lo &Jui-Hung Hung ...
The work carried out in this paper focused on “Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines”. The aim of this work is to develop and verify the ability of machine learning models to solve the problem of estimating the...
While existing machine learning models have demonstrated efficiency and accuracy in overpressure prediction, they fall short in providing full-field spatiotemporal predictions of pressure wave propagations, essential for comprehensive blast simulations. Our GNN-based approach addresses these limitations by ...
This question has no one-size-fits-all answer, as the best framework for machine learning will depend on your specific needs and goals. However, a few popular frameworks are widely used in the field of machine learning, such as TensorFlow, PyTorch, and Keras. ...
Kashefi, A., Rempe, D. & Guibas, L. J. A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries.Phys. Fluids33, 027104 (2021). ArticleADSGoogle Scholar Li, Z. et al. Fourier neural operator for parametric partial differential equations. inInt. Con...
The Brain Activity Flow ("Actflow") Toolbox. Tools to quantify the relationship between connectivity and task activity through network simulations and machine learning prediction. Helps determine how connections contribute to specific brain functions. -
A predictive machine learning force-field framework for liquid electrolyte development A machine learning force-field framework is proposed to predict the density, viscosity and ionic conductivity of liquid electrolytes with accuracy that is higher than classical force fields. ...
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of data-science-inspired work. The dawn of computational databases has made the integration of analysis, prediction and discovery the key theme in accelerated alloy research. Advances in machine-learning methods and...