Results 81 to 90 of about 139,430 (360)

Immune Predictors of Radiotherapy Outcomes in Cervical Cancer

Advanced Science, EarlyView.
This study reveals dynamic immune remodeling in cervical cancer following radiotherapy. Single‐cell analysis identifies the C3/C3AR1 axis as a central mediator of epithelial–myeloid crosstalk, whose inhibition reduces treatment efficacy in mice. Guided by these insights, the eight‐feature machine‐learning model: Cervical Cancer Radiotherapy Immune ...
Linghao Wang, Jie Zhu, Zequn Ding, Zhiyuan Xie, Xingchen Liu, Feihong Zhang, Xiaojun Liu, Yan Zhang, Haiyan Chen   +8 more
wiley   +1 more source

Comprehensive Profiling of N6‐methyladnosine (m6A) Readouts Reveals Novel m6A Readers That Regulate Human Embryonic Stem Cell Differentiation

Advanced Science, EarlyView.
This research deciphers the m6A transcriptome by profiling its sites and functional readout effects: from mRNA stability, translation to alternative splicing, across five different cell types. Machine learning model identifies novel m6A‐binding proteins DDX6 and FXR2 and novel m6A reader proteins FUBP3 and L1TD1.
Zhou Huang, Rucong Liu, Zibaguli Wubulikasimu, Wanqing Zhao, Jiaqi Huang, Jiaxuan Wang, Tianyuan Zhang, Rui Fan, Wei Kong, Qinghua Cui, Yang Li, Yuan Zhou   +11 more
wiley   +1 more source

Multi‐Omics Reveal the Dysregulated Gut‐Joint Axis in Knee Synovitis: Data from Two Osteoarthritis Studies in China

Advanced Science, EarlyView.
The “gut‐joint axis” in knee synovitis is uncovered. Integrated multi‐omics studies are conducted in two independent osteoarthritis cohorts. Synovitis is characterized an increased F/B ratio, as well as alterations of 3‐HIA, geranic acid, and TWEAK. Upregulated TWEAK receptor is found in high‐grade synovitis, and inversely correlated with lower TWEAK ...
Xiaoshuai Wang, Yi Liu, Ziying Sun, Jia Li, Zhihao Lu, Jiayuan Huang, Shixian Hu, Peihua Cao, Xiaolong Cao, Shangru Li, Jianzhao Ruan, Jinhui Liu, Jingyu Xie, Haitao Sun, Tianyu Chen, Shengfa Li, Zhaohua Zhu, Zhibo Wen, Rocky S. Tuan, David John Hunter, Zhong Alan Li, Dongquan Shi, Changhai Ding   +22 more
wiley   +1 more source

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers [PDF]

, 2022
Xianbin Song, Jiangang Zhu, Xiaoli Tan, Wenlong Yu, Qianqian Wang, Dongfeng Shen, Wenyu Chen   +6 more
openalex   +1 more source

MODELLING OVERDISPERSED SEED GERMINATION DATA: XGBOOST'S PERFORMANCE

The Journal of Animal and Plant Sciences, 2023
Depending on the extent of variability in germination count data, the problem of overdispersion arises. This problem causes significant problems in estimation. In this study, gradient boosting algorithms are used as a new approach to support precision agriculture applications in estimating overdispersed germination counts.
Ser, Gazel, Bati, Cafer Tayyar
openaire   +2 more sources

CLinNET: An Interpretable and Uncertainty‐Aware Deep Learning Framework for Multi‐Modal Clinical Genomics

Advanced Science, EarlyView.
Identifying disease‐causing genes in neurocognitive disorders remains challenging due to variants of uncertain significance. CLinNET employs dual‐branch neural networks integrating Reactome pathways and Gene Ontology terms to provide pathway‐level interpretability of genomic alterations.
Ivan Bakhshayeshi, Mohammad Mahdi Hosseini, Ahmadreza Argha, Roxana Zahedi, Nigel H. Lovell, Hamid Alinejad‐Rokny   +5 more
wiley   +1 more source

Predicting stellar rotation periods using XGBoost

Astronomy & Astrophysics
Context. The estimation of rotation periods of stars is a key challenge in stellar astrophysics. Given the large amount of data available from ground-based and space-based telescopes, there is a growing interest in finding reliable methods to quickly and automatically estimate stellar rotation periods with a high level of accuracy and precision.
Gomes, Nuno R. C., Del Sordo, Fabio, Torgo, Luís   +2 more
openaire   +3 more sources

Hyperparameter Optimization with Genetic Algorithms and XGBoost: A Step Forward in Smart Grid Fraud Detection

Italian National Conference on Sensors
This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications.
Adil Mehdary, Abdellah Chehri, A. Jakimi, R. Saadane   +3 more
semanticscholar   +1 more source

scPER: A Rigorous Computational Approach to Determine Cellular Subtypes in Tumors Aligned With Cancer Phenotypes From Total RNA Sequencing

Advanced Science, EarlyView.
scPER presents an adversarial‐autoencoder framework that deconvolves bulk total RNA‐seq to quantify tumor‐microenvironment cell types and uncover phenotype‐linked subclusters. Across diverse benchmarks, scPER improves accuracy over existing tools.
Bingrui Li, Xiaobo Zhou, Raghu Kalluri
wiley   +1 more source

Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Health risk classification is important. However, health risk classification is challenging to address using conventional analytical techniques.
Syaiful Anam, Imam Nurhadi Purwanto, Dwi Mifta Mahanani, Feby Indriana Yusuf, Hady Rasikhun   +4 more
doaj   +1 more source