Results 111 to 120 of about 1,171,833 (351)

Unsupervised machine learning for physical concepts

, 2022
In recent years, machine learning methods have been used to assist scientists in scientific research. Human scientific theories are based on a series of concepts. How machine learns the concepts from experimental data will be an important first step.
openaire   +2 more sources

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

Exploring high opioid prescriptions among nephrologists in the United States using machine learning algorithms

Emerging Trends in Drugs, Addictions, and Health
Background and aims: The opioid pandemic has contributed to deaths globally, and prescription opioids have played a crucial role in these deaths. Addressing overdose requires understanding the reasons behind prescription, especially in cases of chronic ...
Shivashankar Basapura Chandrashekarappa, Sulaf Assi, Manoj Jayabalan, Abdullah Al-Hamid, Dhiya Al-Jumeily   +4 more
doaj   +1 more source

Loss of VMP1 Impairs Tight Junction Recycling and Aggravates Intestinal Barrier Dysfunction in Inflammatory Bowel Disease

Advanced Science, EarlyView.
This study identifies vacuole membrane protein 1 (VMP1) as a critical regulator of intestinal epithelial barrier homeostasis. VMP1 facilitates the recruitment of CORO1C to late endosomes, supporting Retromer‐mediated recycling of the tight junction protein Occludin.
Jiawei Zhao, Jianjun Zou, Chen Zhou, Yingui Wang, Yiman Liu, Yanqing Zhou, Yuxiang Wang, Xinyu Zhang, Huishu Yang, Hongjie Yin, Dongsheng Bai, Yue Zhao, Na Lu   +12 more
wiley   +1 more source

Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study

Journal of Cardiovascular Development and Disease
Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. This study investigates the utility of machine learning algorithms in predicting stroke and identifying key risk factors using data from the Suita study,
Thien Vu, Yoshihiro Kokubo, Mai Inoue, Masaki Yamamoto, Attayeb Mohsen, Agustin Martin-Morales, Takao Inoué, Research Dawadi, Michihiro Araki   +8 more
doaj   +1 more source

A demonstration of unsupervised machine learning in species delimitation.

Molecular Phylogenetics and Evolution, 2019
One major challenge to delimiting species with genetic data is successfully differentiating population structure from species-level divergence, an issue exacerbated in taxa inhabiting naturally fragmented habitats.
S. Derkarabetian, Stephanie Castillo, Peter K. Koo, S. Ovchinnikov, M. Hedin   +4 more
semanticscholar   +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

Spending pattern visualization using unsupervised machine learning

, 2023
Gabriel Porto Oliveira, Jadson Castro Gertrudes, Roberta B. Oliveira   +2 more
openalex   +2 more sources

Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model

Advanced Science, EarlyView.
Understanding protein sequence–function relationships remains challenging due to poorly defined motifs and limited residue‐level annotations. An annotation‐agnostic framework is introduced that segments protein sequences into “protein words” using attention patterns from protein language models.
Hedi Chen, Jingrui Zhong, Xiaochun Zhang, Jingke Chen, Lin Guo, Xiaoliang Xiong, Xiaonan Zhang, Xiangyu Liu, Bailong Xiao, Boxue Tian   +9 more
wiley   +1 more source

A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods

, 2021
Hüseyin Fidan, Mehmet Erkan Yüksel
openalex   +2 more sources