Results 151 to 160 of about 85,077 (292)

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

Case Based Learning for Structure of Decision Tree and Application to Human Assessment Knowledge

, 2006
Toshio Tsuchiya, Norihiko KIKKAWA, Yukihiro Matsubara, Noriyuki Iwane   +3 more
openalex   +2 more sources

Comparison of Machine Learning Methods for the Detection of Focal Cortical Dysplasia Lesions: Decision Tree, Support Vector Machine and Artificial Neural Network [PDF]

, 2021
Zohreh Ganji, Seyed Amir Zamanpour, Hoda Zare   +2 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

Pronunciation error detection for computer-assisted language learning system based on error rule clustering using a decision tree

, 2007
Akinori Ito, Yen-Ling Lim, Motoyuki Suzuki, Shozo Makino   +3 more
openalex   +2 more sources

Learning a fuzzy decision tree from uncertain data [PDF]

, 2017
Hang Yu, Jie Lü, Guangquan Zhang
openalex   +1 more source

Learning with continuous piecewise linear decision trees [PDF]

, 2020
Qinghua Tao, Li Zhen, Jun Xu, Na Xie, Shuning Wang, Johan A. K. Suykens   +5 more
openalex   +1 more source

Active Sites‐Enriched Hierarchical Weyl Semimetal WTe2 Nanowire Arrays for Highly Efficient Hydrogen Evolution Reaction

Advanced Science, EarlyView.
Weyl semimetal WTe2 possesses high conductivity and robust topological surface states. A controlled synthesis method for carbon fiber‐supported WTe2 and core–shell WO3−x–WTe2 nanowire arrays is proposed. The WTe2 nanowires are highly crystalline, uniform, and vertically aligned, providing abundant active sites and durability in hydrogen evolution ...
Hyeonkyeong Kim, Youngdong Yoo
wiley   +1 more source

Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti

Results in Engineering
At this time, as the world and nations move to reduce the use of fossil fuels, research is oriented toward improving the energy consumption of people and buildings.
Fathi Farah Fadoul, Abdoulaziz Ahmed Hassan, Ramazan Çağlar   +2 more
doaj  

A SYSTEM OF LEARNING LINKS BETWEEN UTTERANCES WITH META-INFORMATION IN CHARACTER BASED MULTI-PARTY CONVERSATION BY DECISION TREE

, 2007
Junpei Nakamura, Yasuhiro Tajima, Yoshiyuki Kotani   +2 more
openalex   +1 more source