Results 191 to 200 of about 155,735 (298)

An Integrative Strategy Delineates Modular Metabolic Remodeling and Potential Therapeutic Targets Across Metabolic Diseases

Advanced Science, EarlyView.
An integrative single‐cell atlas across multiple metabolic diseases reveals coordinated metabolic modules and disease‐shared versus disease‐specific pathway activities. By systematically comparing scoring strategies, a robust RankAve framework is established. Coupled with network analysis and drug‐target prediction, this resource uncovers cross‐disease
Kuan Yang, Congxue Hu, Hang Zhao, Xiao Peng, Yiming Yan, Jiasi Wang, Wenqi Jiang, Depeng Mu, Yan Cao, Xia Li, Yunpeng Zhang   +10 more
wiley   +1 more source

Dynamic Ensemble Learning with Transfer Learning for Fatigue Performance Prediction in Ni-Based Superalloys. [PDF]

Materials (Basel)
Yang J, Du F, Lv H, Li W, Wu D.
europepmc   +1 more source

Advancing the Design of High‐Efficiency Printable Hole‐Conductor‐Free Mesoscopic Perovskite Solar Cells Through Machine Learning

Advanced Science, EarlyView.
Based on the largest printable mesoscopic perovskite solar cells database we established, stacking model achieved precise PCE prediction (R2 = 0.73, MAE = 2.18%). Multiple experiments verified the accuracy of the model, which guided the fabrication of high‐PCE devices with an efficiency of 19.36%.
Hao Meng, Jingzi Zhang, Xu Zhu, Yuelin Wang, Antai Yang, Kailong Hu, Chengquan Zhong, Jiakai Liu, Menghan Dun, Xi Lin   +9 more
wiley   +1 more source

A disease-agnostic approach to ensemble learning for infectious disease forecasting. [PDF]

Nat Commun
Murph AC, Beesley LJ, Gibson GC, Castro LA, Del Valle SY, Osthus D.   +5 more
europepmc   +1 more source

Machine‐Learning Framework for Designing Stable Interfaces in All‐Solid‐State Lithium‐Ion Batteries

Advanced Science, EarlyView.
A data‐driven strategy is developed to discover coating materials for all‐solid‐state lithium batteries. Using calculations of interfacial reactivity, unsupervised pattern recognition, and machine‐learning prediction, the study identifies low‐reactivity compositional patterns and screens new lithium‐based oxide and polyanion candidates, extending ...
Sehyeok Park, Myeongcho Jang, Hun‐Gi Jung, Kyung Yoon Chung, Seungho Yu   +4 more
wiley   +1 more source

Subway door fault prediction employing stacking ensemble learning. [PDF]

Sci Rep
Song H, Tang S, Xia J, Zhang L, Kang H, Li P.   +5 more
europepmc   +1 more source

Deep Learning Network‐Tailored Microenvironment Matching of 4D Bioprinting Bioactive Scaffolds for Bone Regeneration

Advanced Science, EarlyView.
A DLN dataset was built to analyze MABS composition versus in vitro/in vivo osteogenesis and angiogenesis. An MLP neural network, taking BG morphological parameters as input, extracts bioactive features from these datasets. A rabbit tibial defect model then validates 4D‐printed MABS for adaptability and bone regeneration in critical defects.
Xiongjie Liang, Yuechi Zhang, Weifeng Hu, Shiyan Lv, Fan Jia, Yan Zhang, Feng Wu, Changjiang Yang, Guanqi Zhen, Jinglong Yan, Xu Cui, Wei Zhao, Guanghua Chen   +12 more
wiley   +1 more source

Automated bone marrow cell classification using ensemble learning: performance, generalization, and clinical interpretability. [PDF]

Front Med (Lausanne)
Mehmood S, Zubair M, Abbas S, Alharbi RM, Alduailij M, Khan MA, Ghazal TM.   +6 more
europepmc   +1 more source

Calcineurin‐Dependent Stress Adaptation Enables Caspofungin Heteroresistance Leading to Stable Resistance in Candida Glabrata

Advanced Science, EarlyView.
Caspofungin heteroresistance is prevalent in clinical Candida glabrata isolates and depends on calcineurin‐mediated stress adaptation. This transient phenotype serves as a reservoir for resistance evolution, enabling the emergence of stable resistant descendants under prolonged drug pressure.
Yanyu Su, Yi Li, Meng Xiao, Dan Guo, David S. Perlin, Erika Shor, Yingchun Xu, Yingxing Li   +7 more
wiley   +1 more source

MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction. [PDF]

Brief Bioinform
Tian H, Yang K, Liu Z, Gao H, Yu J, Zhang L, Zhou X.   +6 more
europepmc   +1 more source