What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Contextual image classification and Data mining.
His Artificial intelligence study frequently draws connections between related disciplines such as Computer vision.
His study in Machine learning is interdisciplinary in nature, drawing from both Structure, Probabilistic logic and Empirical research.
His studies in Contextual image classification integrate themes in fields like Vector quantization, Deep learning, Coding and Kernel.
In his work, Image segmentation and Histogram is strongly intertwined with Caltech 101, which is a subfield of Neural coding.
His Feature extraction research incorporates themes from Artificial neural network and Convolutional neural network.
His most cited work include:
- 3D Convolutional Neural Networks for Human Action Recognition (3323 citations)
- Linear spatial pyramid matching using sparse coding for image classification (2669 citations)
- Locality-constrained Linear Coding for image classification (2464 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Machine learning, Pattern recognition, Internal medicine and Data mining.
His research ties Computer vision and Artificial intelligence together.
In the subject of general Machine learning, his work in Convolutional neural network is often linked to Preference learning, thereby combining diverse domains of study.
His study connects Feature and Pattern recognition.
Kai Yu has included themes like Inference, Cluster analysis, Probabilistic logic, Information retrieval and Collaborative filtering in his Data mining study.
His studies deal with areas such as Coding and Kernel as well as Contextual image classification.
He most often published in these fields:
- Artificial intelligence (42.35%)
- Machine learning (20.92%)
- Pattern recognition (20.41%)
What were the highlights of his more recent work (between 2015-2021)?
- Genome-wide association study (15.31%)
- Computational biology (5.61%)
- Genetics (12.24%)
In recent papers he was focusing on the following fields of study:
His main research concerns Genome-wide association study, Computational biology, Genetics, Internal medicine and Genetic association.
The concepts of his Genome-wide association study study are interwoven with issues in Expression quantitative trait loci, Genetic predisposition and Locus.
As a member of one scientific family, he mostly works in the field of Internal medicine, focusing on Endocrinology and, on occasion, Colorectal cancer, Prostate cancer, Carcinogenesis, Oncology and Cancer screening.
His Genetic association research integrates issues from SNP, Outcome, Mendelian Randomization Analysis and Heritability.
Kai Yu studied SNP and Correlation and dependence that intersect with Data mining.
Many of his research projects under Single-nucleotide polymorphism are closely connected to Nasopharyngeal carcinoma with Nasopharyngeal carcinoma, tying the diverse disciplines of science together.
Between 2015 and 2021, his most popular works were:
- Massively parallel reporter assays of melanoma risk variants identify MX2 as a gene promoting melanoma. (116 citations)
- Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data. (65 citations)
- Sleep Duration and Cancer in the NIH-AARP Diet and Health Study Cohort. (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Genome-wide association study, Internal medicine, Computational biology, Cancer and Summary data.
His Genome-wide association study study introduces a deeper knowledge of Genetics.
In his study, Candidate gene, Cancer screening, Oncology, Carcinogenesis and Prostate cancer is strongly linked to Endocrinology, which falls under the umbrella field of Internal medicine.
The study incorporates disciplines such as Single-nucleotide polymorphism, Linkage disequilibrium and Genetic association in addition to Computational biology.
The Single-nucleotide polymorphism study combines topics in areas such as Quantitative trait locus, Cancer research and In silico.
Kai Yu has researched Cancer in several fields, including Lung cancer and Hazard ratio.
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.
