Results 71 to 80 of about 2,644 (209)

Biochemically Constrained Multi‐Omics Integration Reveals Protein–Metabolite Dependencies Across Diseases

Advanced Science, EarlyView.
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao, Na Zhou, Ruotong Liu, Xiaofei Li, Jian Li, Fuzhong Xue, Qingzhen Hou   +6 more
wiley   +1 more source

SWTA-Shapley: an efficient contribution evaluation method for federated learning

Dianxin kexue
Federated learning has effectively addressed the “data silo” issue caused by data privacy protection. To maintain the long-term operation of federated learning systems, it is necessary to attract high-quality data owners to participate in federated ...
BAO Shihao, NI Zhengwei
doaj   +2 more sources

Machine Learning‐Assisted Design and Performance Prediction of a Compact Dual‐Band Polarization‐Insensitive THz Metamaterial Absorber for Skin‐Cancer‐Related Refractive‐Index Sensing

Advanced Electronic Materials, EarlyView.
A compact QASRR‐based THz metamaterial absorber enables polarization‐insensitive dual‐band absorption and skin‐cancer‐related refractive‐index sensing through measurable resonance shifts. Field, surface‐current, and circuit analyses clarify the dual‐resonance mechanism, while StackNet‐assisted prediction accurately estimates the simulated absorption ...
Md. Murad Kabir Nipun, S. M. Rifatur Rana, Md. Golam Morshed, Imrose Jahan, F. A. Sabbir Ahamed, Md. Jahedul Islam   +5 more
wiley   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

Visualization of explainable artificial intelligence for GeoAI

Frontiers in Computer Science
Shapley additive explanations are a widely used technique for explaining machine learning models. They can be applied to basically any type of model and provide both global and local explanations.
Cédric Roussel
doaj   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Shapley ratings in brain networks

Frontiers in Neuroinformatics, 2007
Recent applications of network theory to brain networks as well as the expanding empirical databases of brain architecture spawn an interest in novel techniques for analyzing connectivity patterns in the brain.
Rolf Kötter, Rolf Kötter, Rolf Kötter, Andrew T Reid, Andrew T Reid, Antje Krumnack, Egon Wanke, Olaf Sporns   +7 more
doaj   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

CLE-SH: Comprehensive Literal Explanation Package for SHapley Values by Statistical Validity

IEEE Access
Recently, SHapley Additive exPlanations (SHAP) has been widely utilized in various research domains. This is particularly evident in application fields, where SHAP analysis serves as a crucial tool for identifying biomarkers and assisting in result ...
Kyungjin Kim, Youngro Lee, Jongmo Seo
doaj   +1 more source

The expected Shapley value

, 2003
A method to allocate the benefits to the players of a cooperative game is the Shapley value. Its computation demands the knowledge of all coaltion worths with certainty. This paper introduces the expected Shapley value, an extension of the Shapley to games were not all the worths are known with certainty.
openaire   +2 more sources