![Xiaoyu Song](https://www.mountsinai.org/files/fad_img_new/195/0000076810082237745641/0000072500064668603731.jpg)
Xiaoyu Song, DrPH
About Me
Dr. Song has substantive research in statistical genetics and quantile regression methods. She has developed various quantile regression and integrative analysis tools for genome-wide association (GWA), gene expression and proteogenomic studies to identify genomic architecture of complex human diseases. In particular, Dr. Song has served on the NCI's Clinical ProteomicTumor Analysis Consortium (CPTAC) to accelerate our understanding of themolecular basis of cancer through the application of large-scale proteogenomics data analysis. She has also served as co-Investigator on two statisticalmethodology grants funded by NIH/NHGRI to introduce quantile regression to GWA, sequencing and eQTL studies. Dr. Song collaborates widely with world renowned researchers from cancer research, GTEx Consortium, HIV/AIDS, adolescent health and reproductive health.
Language
Position
Research Topics
Biostatistics, Cancer Genetics, Genomics, HIV/AIDS, Proteomics, Public Health
About Me
Dr. Song has substantive research in statistical genetics and quantile regression methods. She has developed various quantile regression and integrative analysis tools for genome-wide association (GWA), gene expression and proteogenomic studies to identify genomic architecture of complex human diseases. In particular, Dr. Song has served on the NCI's Clinical ProteomicTumor Analysis Consortium (CPTAC) to accelerate our understanding of themolecular basis of cancer through the application of large-scale proteogenomics data analysis. She has also served as co-Investigator on two statisticalmethodology grants funded by NIH/NHGRI to introduce quantile regression to GWA, sequencing and eQTL studies. Dr. Song collaborates widely with world renowned researchers from cancer research, GTEx Consortium, HIV/AIDS, adolescent health and reproductive health.
Language
Position
Research Topics
Biostatistics, Cancer Genetics, Genomics, HIV/AIDS, Proteomics, Public Health