Results 221 to 230 of about 110,849 (310)

Control flow graph based code optimization using graph neural networks. [PDF]

Front Robot AI
Peker M, Ozturk O.
europepmc   +1 more source

Functional Mapping of Neurodevelopmental Disease Pathways to Key Neurodevelopmental Processes Represented in the Developmental Neurotoxicity In Vitro Testing Battery

Advanced Science, EarlyView.
Human‐relevant methods are essential for modern chemical safety assessment. This study helps define the capabilities and boundaries of an in vitro testing battery for developmental neurotoxicity by exploring its biological applicability domain. By linking neurodevelopmental disease‐related pathways to key neurodevelopmental processes, the work enhances
Eliska Kuchovska, Kristina Bartmann, Georgea Raad, Mats Schade, Luiz Ladeira, Arif Dönmez, Jördis Klose, Nicolai Görts, Denis Polozij, Lynn‐Christin Saborowski, Farina Bendt, Bernard Staumont, Liesbet Geris, Katharina Koch, Ellen Fritsche   +14 more
wiley   +1 more source

Intelligent scheduling and resource allocation for urban air mobility networks based on graph neural networks. [PDF]

Sci Rep
Xu X, Zhao Y, Zhang P.
europepmc   +1 more source

Endoplasmic Reticulum Geometry Dictates Neuronal Bursting via Calcium Store Refill Rates and Exposes Selective Neuronal Vulnerability

Advanced Science, EarlyView.
The ER's continuous tubular network is maintained by ER‐shaping proteins whose mutation or dysregulation contributes to neurodegenerative diseases. Here, we show that ER morphology sets the speed of Ca2+ store replenishment between firing events. Disrupting ER continuity slows intra‐ER Ca2+ redistribution from extracellular refill (SOCE) sites, driving
Valentina Davi, Pierre Parutto, Yuyi Zhang, Tasuku Konno, Cecile Crapart, Raquel Pereira, John P. Franklin, Mosab Ali Awadelkareem, Daniel C Maddison, Michael J. Devine, Edgar R. Gomes, Joseph Chambers, Elena Koslover, Edward Avezov   +13 more
wiley   +1 more source

Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges. [PDF]

J Chem Inf Model
Medina H, Drake R.
europepmc   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

Representation learning of crystal materials using Graph Neural Networks: Passive symmetry challenges and advances. [PDF]

Sci Prog
Cui J, Han C, Liang J, Li L, Wang F.
europepmc   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

SMART: spatial multi-omic aggregation using graph neural networks and metric learning. [PDF]

Nat Commun
Du Z, Chen Q, Huang W, Chen J, Zheng X.
europepmc   +1 more source