Results 241 to 250 of about 989,104 (320)

A nomogram and random forest model for predicting liver metastasis in patients with early-onset colorectal cancer. [PDF]

Sci Rep
Han X, Bai X, Zhang Q, Qian X.
europepmc   +1 more source

Deep Learning Approach for Predicting Efficiency in Organic Photovoltaics from 2D Molecular Images of D/A Pairs

Advanced Theory and Simulations, EarlyView.
This study highlights the potential of deep learning, particularly Convolutional Neural Networks (CNNs), for predicting the photovoltaic performance of organic solar cells. By leveraging 2D images representing donor/acceptor molecular pairs, the model accurately estimates key performance indicators proving that this image‐based approach offers a fast ...
Khoukha Khoussa, Patrick Lévêque, Larbi Boubchir   +2 more
wiley   +1 more source

Assessing Random Forest self-reproducibility for optimal short biomarker signature discovery. [PDF]

Brief Bioinform
Debit A, Poulet C, Josse C, Jerusalem G, Azencott CA, Bours V, Van Steen K.   +6 more
europepmc   +1 more source

First‐Principles Structure–Activity Relationship Insights Into Phenolic Scaffolds: QSAR Modeling and Drug‐Likeness Screening

Advanced Theory and Simulations, EarlyView.
Integrated machine learning framework for phenolic derivatives: classification (toxicity) and regression (logP) models identify top drug‐like compounds. Random Forest outperformed for toxicity, while Linear Regression best predicted logP. A weighted scoring approach prioritized five safe, lipophilicity‐optimized candidates, supporting rational ...
Houria Nacer, Sridhar Priyankha, Gopal Hema, Anouar Zerouali, Hamza Haddag, Muneerah Mogren Al Mogren, Muthuramalingam Prakash, Majdi Hochlaf   +7 more
wiley   +1 more source

Optimizing public health management with predictive analytics: leveraging the power of random forest. [PDF]

Front Big Data
Wang H, Song Y, Bi H.
europepmc   +1 more source

VDLIN: A Deep Learning‐Based Platform for Methylcobalamin‐Inspired Immunomodulatory Compound Screening

Advanced Science, EarlyView.
Using the convolutional neural network model VDLIN, Co7 is identified as a promising therapeutic candidate. Co7 demonstrates distinct advantages over MCB by effectively balancing anti‐inflammatory and immune‐stimulatory functions, making it a potential novel approach for immune modulation.
Xuefei Guo, Yang Zhao, Xianle Rong, Chenxi Niu, Shiyu Hu, Qiang Liu, Fuping You   +6 more
wiley   +1 more source

Correction: A random forest dynamic threshold imputation method for handling missing data in cognitive diagnosis assessments. [PDF]

Front Psychol
You X, Yang J, Xu X.
europepmc   +1 more source

Genome‐Wide by Lifetime Environment Interaction Studies of Brain Imaging Phenotypes

Advanced Science, EarlyView.
This study explores genome‐wide by lifetime environment interactions on brain imaging phenotypes. Gene‐environment interactions explain more phenotypic variance than main effects, pinpoint regulatory variants, and reveal exposure‐specific biological pathways.
Sijia Wang, Meiyun Wang, Peng Zhang, Jingliang Cheng, Longjiang Zhang, Wenzhen Zhu, Shijun Qiu, Zuojun Geng, Guangbin Cui, Yongqiang Yu, Weihua Liao, Xi‐Nian Zuo, Hui Zhang, Bo Gao, Xiaojun Xu, Tong Han, Zhenwei Yao, Quan Zhang, Feng Liu, Qiang Xu, Jiayuan Xu, Jilian Fu, Nana Liu, Yuan Ji, Jie Tang, Lining Guo, Mengge Liu, Xiaoxiao Xiao, Xiaoxuan Liu, Wei Li, Caihong Wang, Wei Wei, Dapeng Shi, Su Lui, Zhihan Yan, Feng Chen, Jing Zhang, Wen Shen, Yanwei Miao, Dawei Wang, Jia‐Hong Gao, Yunjun Yang, Kai Xu, Junfang Xian, Bing Zhang, Xiaochu Zhang, Zhaoxiang Ye, Le Yu, Wen Qin, Meng Liang, Chunshui Yu, the CHIMGEN Consortium   +51 more
wiley   +1 more source

Status and influencing factors of academic burnout among undergraduate nursing students based on random forest modeling: a cross-sectional study. [PDF]

BMC Nurs
Li T, Shi X, Wang J, Yu M, Li F, Xu H, An Q, Ma J.   +7 more
europepmc   +1 more source

Predicting Immunotherapy Outcomes in NSCLC Using RNA and Pathology from Multicenter Clinical Trials

Advanced Science, EarlyView.
LIRA, a machine learning‐based model, is developed using transcriptomic data from 891 NSCLC patients in the OAK and POPLAR cohorts. Its predictive performance is validated in multiple external cohorts. Patients stratified by LIRA‐score exhibit distinct clinical characteristics and tumor microenvironment profiles.
Zhaojun Wang, Yiran Fang, Xiatong Huang, Guichuang Ma, Qianqian Mao, Xiansheng Lu, Guangda Rong, Yunfang Yu, Yuanyuan Wang, Zhenhua Huang, Huiying Sun, Jiani Wu, Wenchao Gu, Na Huang, Jianhua Wu, Rui Zhou, Xiaoxiang Rong, Siting Zheng, Shaowei Li, Gaofeng Wang, Ling Wang, Wenjun Qiu, Luyang Jiang, Peng Luo, Yonggang Liu, Jianping Bin, Yulin Liao, Min Shi, Zuqiang Wu, Jiguang Wang, Wangjun Liao, Gang Chen, Dongqiang Zeng   +32 more
wiley   +1 more source