Results 111 to 120 of about 3,745,728 (280)

Feature Selection via Coalitional Game Theory [PDF]

, 2007
We present and study the contribution-selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the multiperturbation shapley analysis (MSA), a framework that relies on game theory to estimate usefulness. The algorithm
Dror, G., Cohen, S. B., Ruppin, E.
core  

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

Pre‐Regenerative Endothelial Cells Empowered by Antler Blood Exosomes Orchestrate Peripheral Nerve Repair

Advanced Science, EarlyView.
The pre‐regenerative vascular niche (PVN) is essential for nerve repair, yet its endothelial blueprint remains unclear. We identified angiogenic ECs (AECs) as the dominant pre‐regenerative subset and found that antler blood–derived exosomes (AB‐EXO) promote repair via IMP3.
Jinsheng Huang, Qiang Chen, Aihua Jin, Xiaoqi Yang, Hui Zhou, Weiquan Gong, Jin Cheng, Jun Dai, Minjin Wang, Meihua Piao, Yuanyi Wang, Nan Zhou   +11 more
wiley   +1 more source

GHRHR Deficiency Enhances Retinal Ganglion Cell Survival and Visual Functions in Experimental Glaucoma by Inhibiting Ferroptosis

Advanced Science, EarlyView.
Glaucoma, a major cause of blindness, involves retinal ganglion cell (RGC) degeneration. This study shows growth hormone‐releasing hormone receptor (GHRHR) deficiency preserves RGC survival and restores vision, unlike activation which only aids survival.
Yan Tong, Ming Ho Yam, Jiaxin Zhang, Lin Du, Linbin Zhou, Yolanda Wong Ying Yip, Bo Man Ho, Hemlata Bisnauthsing, Jiahui Li, Ivan Kong, Shushu Xu, Changzhen Fu, Karl K.H. So, Joaquim S.L. Vong, Ling‐Ping Cen, Ming‐Ming Yang, Khazeema Yousaf, Mai Har Sham, Sun On Chan, Poemen P. Chan, Chi Pui Pang, Clement C. Tham, Jing Na He, Jian Li, Wai Kit Chu   +24 more
wiley   +1 more source

Localized Feature Selection For Unsupervised Learning [PDF]

, 2010
Clustering is the unsupervised classification of data objects into different groups (clusters) such that objects in one group are similar together and dissimilar from another group.
Li, Yuanhong
core   +1 more source

An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification. [PDF]

Sensors (Basel), 2021
Jin B, Fu C, Jin Y, Yang W, Li S, Zhang G, Wang Z.   +6 more
europepmc   +1 more source

Feature subset selection: a correlation based filter approach [PDF]

, 1997
Recent work has shown that feature subset selection can have a position affect on the performance of machine learning algorithms. Some algorithms can be slowed or their performance adversely affected by too much data some of which may be irrelevant or ...
Hall, Mark A., Smith, Lloyd A.
core  

Integrative Multi‐Omics Analysis Reveals a Mitochondrial–Immune Axis Associated With Neoadjuvant Chemotherapy Response in High‐Grade Serous Ovarian Cancer

Advanced Science, EarlyView.
Integrative multi‐omics analysis delineates a mitochondrial–immune axis governing neoadjuvant chemotherapy response in high‐grade serous ovarian cancer. Immune‐active tumors exhibit enhanced B‐cell infiltration and favorable sensitivity, whereas metabolically rewired tumors display oxidative phosphorylation dependency and resistance.
Wei Jiang, Yi Wang, Xiaohang Lu, Qianlan Yao, Libing Xiang, Xu Cai, Xiaoyu Tu, Shaoxian Tang, Ying Xu, Yufan Cheng, Liming Shen, Huijuan Yang   +11 more
wiley   +1 more source

Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization

Applied Sciences
Multi-source heterogeneous data has been widely adopted in developing artificial intelligence systems in recent years. In real-world scenarios, raw multi-source data are generally unlabeled and inherently contain multi-view noise and feature redundancy ...
Yifan Shi, Haixin Zeng, Xinrong Gong, Lei Cai, Wenjie Xiang, Qi Lin, Huijie Zheng, Jianqing Zhu   +7 more
doaj   +1 more source

The Unsupervised Feature Selection Algorithms Based on Standard Deviation and Cosine Similarity for Genomic Data Analysis. [PDF]

Front Genet, 2021
Xie J, Wang M, Xu S, Huang Z, Grant PW.
europepmc   +1 more source