2018 - Member of Academia Europaea
2018 - Fellow of the Royal Academy of Engineering (UK)
Yike Guo mostly deals with Artificial intelligence, Asthma, Cloud computing, Immunology and Data science. His work is dedicated to discovering how Artificial intelligence, Pattern recognition are connected with Fully automatic and other disciplines. His Asthma research integrates issues from Severity of illness, Cohort and Allergy.
His Cloud computing study combines topics in areas such as Virtual machine, Distributed computing, State, Provisioning and Focus. His study in the fields of Innate lymphoid cell and Interleukin 5 under the domain of Immunology overlaps with other disciplines such as Exhaled nitric oxide. His Data science research is multidisciplinary, incorporating elements of Bioinformatics, Database transaction, Cultural diversity, Knowledge extraction and Big data.
Yike Guo spends much of his time researching Artificial intelligence, Data science, Machine learning, Data mining and Immunology. His studies in Artificial intelligence integrate themes in fields like Natural language processing and Pattern recognition. His study of Segmentation is a part of Pattern recognition.
He focuses mostly in the field of Data science, narrowing it down to topics relating to Big data and, in certain cases, Cloud computing. His Immunology study integrates concerns from other disciplines, such as Transcriptome and Cohort. His study in Allergy extends to Asthma with its themes.
His primary scientific interests are in Artificial intelligence, Data assimilation, Machine learning, Immunology and Asthma. His Artificial intelligence research includes elements of Brain activity and meditation and Natural language processing. The various areas that Yike Guo examines in his Machine learning study include Dynamical system, Process, Task and State.
His work on Inflammasome and Allergy as part of general Immunology study is frequently connected to FURIN Gene and TMPRSS2, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His research in Asthma intersects with topics in Transcriptome, Quality of life, COPD, Disease and Socioeconomic status. His biological study spans a wide range of topics, including Data mining, Convolutional neural network, Generative adversarial network and Complex dynamics.
Yike Guo focuses on Artificial intelligence, Data assimilation, Algorithm, Artificial neural network and Immunology. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Relation. The study incorporates disciplines such as Flow, Data-driven, Basis function and Computer simulation in addition to Algorithm.
His work on Recurrent neural network as part of general Artificial neural network study is frequently linked to Consumption, bridging the gap between disciplines. In general Immunology study, his work on Asthma and Inflammasome often relates to the realm of Proteases and TMPRSS2, thereby connecting several areas of interest. The concepts of his Deep learning study are interwoven with issues in Time complexity, Errors-in-variables models, Data mining and Lorenz system.
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.
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
Guang Yang;Simiao Yu;Hao Dong;Greg Slabaugh.
IEEE Transactions on Medical Imaging (2018)
DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG
Akara Supratak;Hao Dong;Chao Wu;Yike Guo.
international conference of the ieee engineering in medicine and biology society (2017)
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
Hao Dong;Guang Yang;Fangde Liu;Yuanhan Mo.
Annual Conference on Medical Image Understanding and Analysis (2017)
Clinical and inflammatory characteristics of the European U-BIOPRED adult severe asthma cohort
Dominick E. Shaw;Ana R. Sousa;Stephen J. Fowler;Louise J. Fleming.
European Respiratory Journal (2015)
Lightweight Resource Scaling for Cloud Applications
Rui Han;Li Guo;Moustafa M. Ghanem;Yike Guo.
cluster computing and the grid (2012)
T-helper cell type 2 (Th2) and non-Th2 molecular phenotypes of asthma using sputum transcriptomics in U-BIOPRED
Chih-Hsi Scott Kuo;Chih-Hsi Scott Kuo;Stelios Pavlidis;Matthew Loza;Fred Baribaud.
European Respiratory Journal (2017)
Biocloud: Cloud Computing for Biological, Genomics, and Drug Design
Ching-Hsien Hsu;Chun-Yuan Lin;Ming Ouyang;Yi Ke Guo.
BioMed Research International (2013)
Air Pollution Monitoring and Mining Based on Sensor Grid in London
Yajie Ma;Mark Richards;Moustafa Ghanem;Yike Guo.
Making sense of big data in health research: Towards an EU action plan
Charles Auffray;Charles Auffray;Rudi Balling;Inês Barroso;László Bencze.
Genome Medicine (2016)
U-BIOPRED clinical adult asthma clusters linked to a subset of sputum omics
Diane Lefaudeux;Bertrand De Meulder;Matthew J. Loza;Nancy Peffer.
The Journal of Allergy and Clinical Immunology (2017)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: