Results 231 to 240 of about 2,065,553 (347)

Unveiling Salt Tolerance Mechanisms in Plants: Integrating the KANMB Machine Learning Model With Metabolomic and Transcriptomic Analysis

Advanced Science, EarlyView.
Salt stress endangers coastal cereal crops, requiring resilient crop solutions. This study employs machine learning (KANMB) to analyze multi‐omics data from halophyte Spartina alterniflora, revealing 226 salt‐stress biomarkers and linking them to tolerance pathways. The MYB gene SaMYB35 regulates flavonoid biosynthesis under salinity.
Shoukun Chen, Hao Zhang, Shuqiang Gao, Kunhui He, Tingxi Yu, Shang Gao, Jiankang Wang, Huihui Li   +7 more
wiley   +1 more source

Machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests: Comment. [PDF]

J Clin Tuberc Other Mycobact Dis
Daungsupawong H, Wiwanitkit V.
europepmc   +1 more source

Likelihood-free frequentist inference: bridging classical statistics and machine learning for reliable simulator-based inference

Niccolò Dalmasso, Luca Masserano, David Zhao, Rafael Izbicki, Ann B. Lee   +4 more
openalex   +1 more source

Immunotyping the Tumor Microenvironment Reveals Molecular Heterogeneity for Personalized Immunotherapy in Cancer

Advanced Science, EarlyView.
This study develops TMEclassifier, a machine‐learning tool that classifies cancers into three distinct subtypes—Immune exclusive (IE), immune suppressive (IS), and immune activated (IA)—which exhibit significant heterogeneity and necessitate customized therapeutic strategies.
Dongqiang Zeng, Yunfang Yu, Wenjun Qiu, Qiyun Ou, Qianqian Mao, Luyang Jiang, Jianhua Wu, Jiani Wu, Huiyan Luo, Peng Luo, Wenchao Gu, Na Huang, Siting Zheng, Shaowei Li, Yonghong Lai, Xiatong Huang, Yiran Fang, Qiongzhi Zhao, Rui Zhou, Huiying Sun, Wei Zhang, Jianping Bin, Yulin Liao, Masami Yamamoto, Tetsuya Tsukamoto, Sachiyo Nomura, Min Shi, Wangjun Liao   +27 more
wiley   +1 more source

Protein interactions, network pharmacology, and machine learning work together to predict genes linked to mitochondrial dysfunction in hypertrophic cardiomyopathy. [PDF]

Sci Rep
Chen JL, Xiao D, Liu YJ, Wang Z, Chen ZH, Li R, Li L, He RH, Jiang SY, Chen X, Xu LX, Lu FC, Wang JM, Shan ZG.   +13 more
europepmc   +1 more source

Stacked ensemble machine learning approach for electroencephalography based major depressive disorder classification using temporal statistics

N. Ahmed, Tejas Kadengodlu Bhat, Omkar S Powar   +2 more
openalex   +1 more source

Distinct CTC Specific RNA Profile Enables NSCLC Early Detection and Dynamic Monitoring of Advanced NSCLC

Advanced Science, EarlyView.
A quantitative CTC RNA assay is developed by incorporating multi‐antibody‐based CTC isolation and specific mRNA quantification using RT‐ddPCR. The NSCLC CTC ScoreD demonstrates high accuracy for early‐stage NSCLC detection, significantly outperforming serum CEA. NSCLC CTC ScoreM exhibits a more accurate early‐warning of responses to different therapies
Xiaoyu Wang, Pi Ding, Wenjuan Xu, Lei Qiu, Jing Ren, Yucheng Fei, Zhili Wang, Cheng Li, Yufei Xing, Mingjing Shen, Yawen Zhu, Yun Guo, Na Sun, Renjun Pei, Minhua Shi   +14 more
wiley   +1 more source

Machine-learning-based prediction of functional recovery in deep-pain-negative dogs after decompressive thoracolumbar hemilaminectomy for acute intervertebral disc extrusion. [PDF]

Vet Surg
Low D, Stables S, Kondrotaite L, Garland B, Rutherford S.   +4 more
europepmc   +1 more source

Lipid discovery enabled by sequence statistics and machine learning

Priya M. Christensen, Jonathan Martin, Aparna Uppuluri, Luke R. Joyce, Yahan Wei, Ziqiang Guan, Faruck Morcos, Kelli L. Palmer   +7 more
openalex   +1 more source

Synergistic Machine Learning Guided Discovery of ABa3(BSe3)2X (A = Rb, Cs; X = Cl, Br, I): A Promising Family as Property‐Balanced IR Functional Materials

Advanced Science, EarlyView.
A synergistic framework for interpretable ML‐assisted target discovery of selenoborates is developed. ABa3(BSe3)2X (A = Rb, Cs; X = Cl, Br, I) are successfully predicted and synthesized, exhibiting significant potential to be promising IR functional materials. This work promotes the cooperation of ML technology and materials science.
Yihan Yun, Mengfan Wu, Zhihua Yang, Guangmao Li, Shilie Pan   +4 more
wiley   +1 more source