Results 111 to 120 of about 109,092 (251)

Data‐Driven Printability Modeling of Hydrogels for Precise Direct Ink Writing Based on Rheological Properties

open access: yesAdvanced Science, EarlyView.
A predictive model for 3D printability is developed by integrating rheological analysis, including the Large Amplitude Oscillatory Shear (LAOS) test, with machine learning. With prediction errors under 10%, the model shows that post‐extrusion recovery controls horizontal printability, while high‐strain‐rate nozzle flow dictates vertical printability ...
Eun Hui Jeong   +7 more
wiley   +1 more source

CellPolaris: Transfer Learning for Gene Regulatory Network Construction to Guide Cell State Transitions

open access: yesAdvanced Science, EarlyView.
CellPolaris decodes how transcription factors guide cell fate by building gene regulatory networks from transcriptomic data using transfer learning. It generates tissue‐ and cell‐type‐specific networks, identifies master regulators in cell state transitions, and simulates TF perturbations in developmental processes.
Guihai Feng   +27 more
wiley   +1 more source

A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters

open access: yesMathematics
This paper introduces a novel hyperparameter optimization framework for regression tasks called the Combined-Sampling Algorithm to Search the Optimized Hyperparameters (CASOH).
Nguyen Huu Tiep   +8 more
doaj   +1 more source

Immune Predictors of Radiotherapy Outcomes in Cervical Cancer

open access: yesAdvanced Science, EarlyView.
This study reveals dynamic immune remodeling in cervical cancer following radiotherapy. Single‐cell analysis identifies the C3/C3AR1 axis as a central mediator of epithelial–myeloid crosstalk, whose inhibition reduces treatment efficacy in mice. Guided by these insights, the eight‐feature machine‐learning model: Cervical Cancer Radiotherapy Immune ...
Linghao Wang   +8 more
wiley   +1 more source

Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence

open access: yesApplied Sciences
Automated machine learning (AutoML) is recognized for its efficiency in facilitating model development due to its ability to perform tasks autonomously, without constant human intervention.
Dagoberto Castellanos-Nieves   +1 more
doaj   +1 more source

Comprehensive Profiling of N6‐methyladnosine (m6A) Readouts Reveals Novel m6A Readers That Regulate Human Embryonic Stem Cell Differentiation

open access: yesAdvanced Science, EarlyView.
This research deciphers the m6A transcriptome by profiling its sites and functional readout effects: from mRNA stability, translation to alternative splicing, across five different cell types. Machine learning model identifies novel m6A‐binding proteins DDX6 and FXR2 and novel m6A reader proteins FUBP3 and L1TD1.
Zhou Huang   +11 more
wiley   +1 more source

Hyperparameter Optimization for Effort Estimation

open access: yes, 2018
Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g.
Xia, Tianpei   +5 more
openaire   +2 more sources

Potent and Long‐Lasting Immunogenicity Generated by LNP‐mRNA gE Antigen Against Varicella Zoster Virus via an AI‐Assisted Pipeline

open access: yesAdvanced Science, EarlyView.
This study designs a novel mRNA‐LNP vaccine targeting VZV glycoprotein E (gE) for herpes zoster via an AI‐assisted pipeline. Validated in mice and rhesus macaques, the mRNA‐LNP vaccine shows strong humoral and cellular immune responses, with CD4+ T‐cell responses more effective and durable than Shingrix, offering a promising prophylactic option ...
Kai Dong   +6 more
wiley   +1 more source

Unpaired Learning‐Enabled Nanotube Identification from AFM Images

open access: yesAdvanced Science, EarlyView.
Identifying nanotubes on rough substrates is notoriously challenging for conventional image analysis. This work presents an unpaired deep learning approach that automatically extracts nanotube networks from atomic force microscopy images, even on complex polymeric surfaces used in roll‐to‐roll printing.
Soyoung Na   +10 more
wiley   +1 more source

A hybrid optimization and data-driven approach to understand the role of the risk-aversion profile parameter in portfolio optimization problems with shorting constraints

open access: yesOperations Research Perspectives
This study contributes to the optimization literature with an approach that would help investors understand how the risk-aversion profile hyperparameter affects excess returns, risk, and Sharpe ratio curves in portfolio optimization problems with short ...
Mariano Carbonero-Ruz   +3 more
doaj   +1 more source

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