What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Operating system
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.
His most cited work include:
- DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (355 citations)
- Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks (274 citations)
- DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG (273 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (28.17%)
- Data science (10.59%)
- Machine learning (11.63%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (28.17%)
- Data assimilation (6.20%)
- Machine learning (11.63%)
In recent papers he was focusing on the following fields of study:
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.
Between 2019 and 2021, his most popular works were:
- Vitamin D Metabolism is Dysregulated in Asthma and Chronic Obstructive Pulmonary Disease (16 citations)
- Data-driven reduced order model with temporal convolutional neural network (14 citations)
- Data-driven reduced order model with temporal convolutional neural network (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Operating system
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.
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