Yufeng Shen
Icahn School of Medicine at Mount Sinai, NY·Genetics
Congenital heart disease (CHD) is the most common birth defect. Genes clearly contribute to it, but which genetic changes cause it, and how, is still not well understood.
- Fields
Genetics
Computer Science
Medicine & Clinical Research
- Topics
congenital heart disease
birth defects
computational genomics
- Grants
- 1 known grant, through Jun 2031
What they work on
- Benchmark, improve and develop machine-learning methods that predict the effects of missense variants, DNA changes that swap a protein's amino acid, in congenital heart disease genomic data.
- Benchmark, improve and develop methods that use large population genome and functional genomics datasets to predict the effects of rare noncoding variants that may change gene regulation during heart development.
- Combine genomic and clinical datasets across the projects of a congenital heart disease program project, and share the methods through a working group and virtual training bootcamps.
More about the research
As more children with CHD survive, their long-term development and health have also become a public health concern. Large genome-sequencing studies are finding new risk genes, but interpreting many of the DNA changes they uncover remains a major bottleneck.
Why it matters
Better ways to judge which rare DNA changes matter could help identify genetic causes of congenital heart disease. Clearer genetic causes could improve diagnosis and predictions of how children and adults with CHD will fare, and could inform future treatment strategies for these patients and their families.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Anat Kreimer
Rutgers University-New Brunswick, NJ·Genetics
Develop methods that integrate massively parallel reporter assay (MPRA) data, which test thousands of DNA sequences at once, with other genomic assays and deep learning to reconstruct cell-type-specific gene regulatory networks and their condition-specific regulators.
Ariadne Letra
University of Pittsburgh, PA·Genetics
Determine the genetic architecture of familial cleft lip and palate (NSCLP) by combining linkage, case-control association and polygenic risk score analyses of whole genome sequences from 172 multigenerational families and additional datasets.
Martin Zhang
Carnegie Mellon University, PA·Genetics
Use multimodal single-cell and spatial genomics data to pinpoint, with high precision, the cell types and cellular contexts most important for diseases identified through genome-wide association studies (GWAS).
The school
Icahn School of Medicine at Mount Sinai: 45 labs on PI Pages, $1.0B a year on research, 62 research doctorates a year.
Reach out
A start for your email. Fill in the brackets.