His primary areas of study are Artificial intelligence, Immunology, Machine learning, Cancer research and Nanotechnology. His works in Deep learning and Deep neural networks are all subjects of inquiry into Artificial intelligence. The concepts of his Deep learning study are interwoven with issues in Test data, Software deployment and Programming paradigm.
His Machine learning research includes elements of Software, Malware and Robustness. Cancer research and Tumor progression are frequently intertwined in his study. His Nanotechnology study combines topics in areas such as Detection limit, Biophysics and Chemical engineering.
His main research concerns Artificial intelligence, Machine learning, Cancer research, Model checking and Distributed computing. His Artificial intelligence research incorporates elements of Computer vision and Pattern recognition. Part of his project on Model checking includes research on Theoretical computer science and Programming language.
His primary areas of investigation include Artificial intelligence, Machine learning, Deep learning, Cancer research and Artificial neural network. His Artificial intelligence research incorporates themes from Computer vision and Pattern recognition. He interconnects Malware and Robustness in the investigation of issues within Machine learning.
Deep learning is often connected to Software in his work. His Cancer research study frequently links to related topics such as Cancer.
His scientific interests lie mostly in Artificial intelligence, Machine learning, Deep learning, Adversarial system and Cancer research. His research integrates issues of Malware, Computer vision and Pattern recognition in his study of Artificial intelligence. His Machine learning research is multidisciplinary, relying on both Classifier and Set.
Yang Liu combines subjects such as Image synthesis, Task analysis, Source code, Software and Robustness with his study of Deep learning. His Software research integrates issues from Software deployment and Computer engineering. The various areas that he examines in his Cancer research study include Reactive oxygen species, Glutathione and Isocitrate dehydrogenase.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
TSC–mTOR maintains quiescence and function of hematopoietic stem cells by repressing mitochondrial biogenesis and reactive oxygen species
Chong Chen;Yu Liu;Runhua Liu;Tsuneo Ikenoue.
Journal of Experimental Medicine (2008)
CD24 and Siglec-10 Selectively Repress Tissue Damage-Induced Immune Responses
Guo-Yun Chen;Jie Tang;Pan Zheng;Yang Liu.
The Therapeutic Effect of Anti-HER2/neu Antibody Depends on Both Innate and Adaptive Immunity
Sae Gwang Park;Sae Gwang Park;Zhujun Jiang;Eric D. Mortenson;Liufu Deng.
Cancer Cell (2010)
PAT: Towards Flexible Verification under Fairness
Jun Sun;Yang Liu;Jin Song Dong;Jun Pang.
computer aided verification (2009)
Targeting HIF1α eliminates cancer stem cells in hematological malignancies.
Yin Wang;Yan Liu;Sami N. Malek;Pan Zheng.
Cell Stem Cell (2011)
DeepGauge: multi-granularity testing criteria for deep learning systems
Lei Ma;Felix Juefei-Xu;Fuyuan Zhang;Jiyuan Sun.
automated software engineering (2018)
graph2vec: Learning Distributed Representations of Graphs
Annamalai Narayanan;Mahinthan Chandramohan;Rajasekar Venkatesan;Lihui Chen.
arXiv: Artificial Intelligence (2017)
FOXP3 is a novel transcriptional repressor for the breast cancer oncogene SKP2
Tao Zuo;Runhua Liu;Huiming Zhang;Xing Chang.
Journal of Clinical Investigation (2007)
Collaborative Security: A Survey and Taxonomy
Guozhu Meng;Yang Liu;Jie Zhang;Alexander Pokluda.
ACM Computing Surveys (2015)
Tendon tissue engineering using scaffold enhancing strategies.
Yang Liu;H.S. Ramanath;Dong-An Wang.
Trends in Biotechnology (2008)
Profile was last updated on December 6th, 2021.
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The ranking h-index is inferred from publications deemed to belong to the considered discipline.
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