Computational biology researcher at Brown wins prestigious early-career NIH award

With a $1.9 million Genomic Innovator Award, Ritambhara Singh will apply machine learning to better understand the genetic underpinnings of disease.

Image of Ritambhara SinghPROVIDENCE, R.I. [Brown University] — Ritambhara Singh, an assistant professor of computer science at Brown University, is one of 11 researchers nationwide to receive a 2021 Genomic Innovator Award from the National Human Genome Research Institute.

The research institute, part of the National Institutes of Health, developed the award to support innovative work by genomics investigators who are early in their careers and play key roles in team-science efforts. Singh’s award, totaling $1.9 million, will support her work developing machine learning approaches to reveal gene regulation mechanisms in diseases.

“I’m excited about continuing and extending our data integration efforts in the lab with this award,” said Singh, who is a member of Brown’s Center for Computational Molecular Biology. “In the era of big data, combining different types of genomic datasets in a way that provides us with important biological insights is a challenging task that we are very keen to tackle in the lab.”

Singh’s research under the award will address fundamental questions in gene regulation studies, specifically revealing the importance of regulatory mechanisms in understanding the genetic basis of disease.

“Our current understanding of gene regulation is akin to solving a jigsaw puzzle,” Singh said. “While many factors governing gene expression have been identified, how these ‘parts’ are pieced together to function as a whole remains unclear. My research has developed and applied state-of-the-art machine learning methods on genomics datasets to attempt to put together these pieces from the data.”

The primary objectives of her proposal include:

  • applying and refining deep learning architectures that capture the underlying structure of complex datasets and connect them to gene expression;
     
  • developing interpretation methods for these models to specifically pick out signals that correlate with misregulated genes in disease cell lines;
     
  • and extending these frameworks to single-cell datasets to understand gene regulation at the single-cell resolution.

“Our efforts will lead to deep learning frameworks that integrate information and produce interpretable outcomes capturing how gene regulation is affected on a broad cell population and single-cell scale,” Singh said. “It’s part of what I see as the ultimate goal for the future of genomics, which is to streamline the flow of information from the genomic data collection effort to our understanding of diseases to the clinical applications. I hope we continue to build collaborations and leverage methodological innovations to accomplish this goal.”

Now in its third year, the Genomic Innovator Award supports researchers whose works span various areas of genomics, including gene-editing technologies, brain-related disorders, single-cell genomics and precision medicine. Unlike more traditional grants that fund rigidly defined research projects, the award provides researchers with the flexibility to pursue innovative research directions in a nimble fashion within a broad scientific area. 

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