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.
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.
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.
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
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A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells
Bradley E. Bernstein;Tarjei S. Mikkelsen;Tarjei S. Mikkelsen;Xiaohui Xie;Michael Kamal.
Genome-wide maps of chromatin state in pluripotent and lineage-committed cells
Tarjei S. Mikkelsen;Manching Ku;Manching Ku;David B. Jaffe;Biju Issac;Biju Issac.
Genome sequence, comparative analysis and haplotype structure of the domestic dog
Kerstin Lindblad-Toh;Claire M Wade;Claire M Wade;Tarjei S. Mikkelsen;Tarjei S. Mikkelsen;Elinor K. Karlsson;Elinor K. Karlsson.
Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals
Xiaohui Xie;Jun Lu;E. J. Kulbokas;Todd R. Golub.
Genome-wide detection and characterization of positive selection in human populations
Pardis C. Sabeti;Pardis C. Sabeti;Patrick Varilly;Patrick Varilly;Ben Fry;Jason Lohmueller.
Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium
Li Jun Ma;H. Charlotte Van Der Does;Katherine A. Borkovich;Jeffrey J. Coleman.
A high-resolution map of human evolutionary constraint using 29 mammals.
Kerstin Lindblad-Toh;Manuel Garber;Or Zuk;Michael F. Lin;Michael F. Lin.
Genomewide analysis of PRC1 and PRC2 occupancy identifies two classes of bivalent domains
Manching Ku;Richard P. Koche;Richard P. Koche;Richard P. Koche;Esther Rheinbay;Esther Rheinbay;Esther Rheinbay;Eric M. Mendenhall;Eric M. Mendenhall.
PLOS Genetics (2008)
Errα and Gabpa/b specify PGC-1α-dependent oxidative phosphorylation gene expression that is altered in diabetic muscle
Vamsi K. Mootha;Christoph Handschin;Dan Arlow;Xiaohui Xie.
Proceedings of the National Academy of Sciences of the United States of America (2004)
Genome of the marsupial Monodelphis domestica reveals innovation in non-coding sequences
Tarjei S. Mikkelsen;Tarjei S. Mikkelsen;Matthew J. Wakefield;Bronwen Aken;Chris T. Amemiya.
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