Results 71 to 80 of about 17,736 (248)

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

Redefining the Health Risk of Battery Materials Through a Biologically Transformed Metal Mixture

Advanced Science, EarlyView.
Inhaled NCM particles undergo lysosomal degradation, releasing complex ion mixtures that induce systemic impact. The impact is determined by a critical balance between antagonistic Ni‐Co interactions and synergistic Mn effects. To capture these complexities in risk assessment, we develop an IAI model, ensuring a more accurate quantitative risk ...
Ze Zhang, Gan Miao, Xueyu Zhang, Zhao Shu, Dawei Lu, Yujie Song, Shanfa Yu, Qian Liu, Yang Song, Rong Zhang, Xiaoting Jin, Yuxin Zheng   +11 more
wiley   +1 more source

Prompt Engineering Accelerates the Data‐Driven Discovery of Photocatalysts via an LLM‐Based Model Ensemble Strategy

Advanced Science, EarlyView.
This work establishes a pipeline that transforms fragmented literature into a structured database for graphitic carbon nitride photocatalyst discovery. A prompt‐engineered, cross‐model large language model ensemble automates high‐fidelity extraction, enabling interpretable machine learning to identify dominant performance descriptors. These data‐driven
Dianyuan Li, Xichen Sun, Shaohua Sun, Runzhou Wang, Miaomiao Zhang, Meng Xiao, Yue Wang, Yuezhou Zhang   +7 more
wiley   +1 more source

Internal Medicine residents use heuristics to estimate disease probability

Canadian Medical Education Journal, 2015
Background: Training in Bayesian reasoning may have limited impact on accuracy of probability estimates. In this study, our goal was to explore whether residents previously exposed to Bayesian reasoning use heuristics rather than Bayesian reasoning to ...
Sen Phang, Pietro Ravani, Jeffrey Schaefer, Bruce Wright, Kevin Mclaughlin   +4 more
doaj  

Customizing Tactile Sensors via Machine Learning‐Driven Inverse Design

Advanced Science, EarlyView.
ABSTRACT Replicating the sophisticated sense of touch in artificial systems requires tactile sensors with precisely tailored properties. However, manually navigating the complex microstructure‐property relationship results in inefficient and suboptimal designs.
Baocheng Wang, Depeng Kong, Zhiao He, Jikai Liang, Yuyao Lu, Zikang Deng, Honghe Li, Mengke Wang, M. Jamal Deen, Zhiqiu Ye, Shuyao Zhou, Huayong Yang, Honghao Lyu, Jun Chen, Kaichen Xu, Geng Yang   +15 more
wiley   +1 more source

Bayesian Informal Logic and Fallacy

Informal Logic, 2004
Bayesian reasoning has been applied formally to statistical inference, machine learning and analysing scientific method. Here I apply it informally to more common forms of inference, namely natural language arguments.
Kevin Korb
doaj   +1 more source

Medical Diagnosis Reimagined as a Process of Bayesian Reasoning and Elimination. [PDF]

Cureus, 2023
Ananda Rao A, Awale M, Davis S.
europepmc   +1 more source

A smart headband for multimodal physiological monitoring in human exercises

Advanced Science, EarlyView.
A novel smart headband incorporating a thermal‐sensation‐based electronic skin is presented for continuous and accurate multimodal physiological monitoring, including pulse waveforms, total metabolic energy expenditure, heart rate, and forehead temperature, across both static and dynamic daily activities.
Shiqiang Liu, Guanxing Chen, Jingyi Peng, Shuo Tian, Qian Mao, Caise Wei, Jinfeng Yuan, Rong Zhu   +7 more
wiley   +1 more source

Differential effects of reward and punishment in decision making under uncertainty: a computational study.

Frontiers in Neuroscience, 2014
Computational models of learning have proved largely successful in characterising potentialmechanisms which allow humans to make decisions in uncertain and volatile contexts.
Elaine eDuffin, Amy Rachel Bland, Alexandre eSchaefer, Marc eDe Kamps   +3 more
doaj   +1 more source

Machine Learning for Designing Perovskites and Perovskite‐Inspired Solar Materials: Emerging Opportunities and Challenges

Advanced Science, EarlyView.
This review offers a comprehensive comparison between perovskites and perovskite‐inspired materials (PIMs), focusing on their crystal structures, electronic properties, and chemical compositions. It evaluates the applicability of machine learning (ML) descriptors and models across both material classes.
Yangfan Zhang, Yiming Xia, Ali Shakiba, Hongrui Zhang, Xiaojing Hao, Priyank V. Kumar, Mahesh P. Suryawanshi   +6 more
wiley   +1 more source