What is he best known for?
The fields of study he is best known for:
- Gene
- Artificial intelligence
- DNA
Xiaohui Xie mostly deals with Genetics, Gene, Genomics, Human genome and Genome.
His work investigates the relationship between Genetics and topics such as Cell biology that intersect with problems in Protein subcellular localization prediction.
His Gene research focuses on Computational biology and how it relates to Lineage, Genetic code, Pseudogene and CTCF.
His Human genome research includes elements of Artificial neural network, Machine learning, Artificial intelligence and Metric.
Xiaohui Xie works mostly in the field of Machine learning, limiting it down to concerns involving Word error rate and, occasionally, Pattern recognition and Dropout.
The Bivalent chromatin study combines topics in areas such as Polycomb-group proteins and Epigenetics.
His most cited work include:
- A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells (4203 citations)
- Genome-wide maps of chromatin state in pluripotent and lineage-committed cells (3420 citations)
- Genome sequence, comparative analysis and haplotype structure of the domestic dog (2011 citations)
What are the main themes of his work throughout his whole career to date?
Xiaohui Xie mainly focuses on Artificial intelligence, Pattern recognition, Genetics, Deep learning and Computational biology.
His study in Genome, Gene, Genomics, Regulation of gene expression and Gene expression profiling falls within the category of Genetics.
Xiaohui Xie interconnects Transcription factor and Cell biology in the investigation of issues within Regulation of gene expression.
His studies deal with areas such as Ground truth, Feature and Computed tomography as well as Deep learning.
His Computational biology research incorporates themes from Human genome, Chromatin, DNA microarray, Cell type and DNA sequencing.
His studies in Chromatin integrate themes in fields like Chromatin immunoprecipitation and Epigenetics.
He most often published in these fields:
- Artificial intelligence (43.52%)
- Pattern recognition (22.80%)
- Genetics (22.28%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (43.52%)
- Pattern recognition (22.80%)
- Deep learning (19.17%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Deep learning, Pose and Artificial neural network.
His biological study deals with issues like Machine learning, which deal with fields such as Lottery.
His Deep learning research includes themes of Radiation therapy, Mean squared error, Task, Computational biology and Computed tomography.
His research in Computational biology intersects with topics in Cell, Multicellular organism and Chromatin, Sequence motif, DNA.
Xiaohui Xie has included themes like Matrix decomposition, Epigenomics and Representation in his Chromatin study.
His DNA study integrates concerns from other disciplines, such as Insertional mutagenesis and Genome.
Between 2018 and 2021, his most popular works were:
- AnatomyNet: Deep learning for fast and fully automated whole‐volume segmentation of head and neck anatomy (141 citations)
- FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. (57 citations)
- Identifying viruses from metagenomic data using deep learning (45 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Gene
- DNA
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Pattern recognition, Artificial neural network and Inference.
His work focuses on many connections between Artificial intelligence and other disciplines, such as Computer vision, that overlap with his field of interest in Normalization and Blood flow.
The various areas that Xiaohui Xie examines in his Deep learning study include Radiation therapy, Viral Identification, Computational biology and Computed tomography.
His Computational biology study also includes fields such as
- Transcription factor, which have a strong connection to Sequence motif, Chromatin, Cell, DNA binding site and ENCODE,
- Genome most often made with reference to Cell type,
- DNA sequencing that intertwine with fields like Contig, Virus, Microbial Genomes, Metagenomics and RefSeq.
His Convolutional neural network study in the realm of Pattern recognition connects with subjects such as Consistency.
His work in Artificial neural network covers topics such as Reduction which are related to areas like Task, End-to-end principle, Pulmonary nodule and Code.
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