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Miriam Barrabes on Muck Rack

Miriam Barrabes

As seen in: Nature, BioRxiv
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Articles

Scalable high resolution ancestry deconvolution for genomic data

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By Helgi Hilmarsson, Arvind Kumar, Miriam Barrabes, Richa Rastogi, Carlos Bustamante, Daniel Mas Montserrat, Alexander G Ioannidis
| Nature Verified
Abstract As genome-wide association studies and genetic risk prediction models extend to globally diverse and admixed biobanks, accurate, scalable ancestry deconvolution, also called local ancestry inference (LAI), has become crucial. LAI assigns ancestry to each genomic segment within an individual, enabling studies of population history and ancestry-associated haplotypic effects. Existing LAI methods scale poorly to biobank-scale data, to the distant past, and to large numbers of ancestries.
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[2511.15977] Efficient Chromosome Parallelization for Precision Medicine Genomic Workflows

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By Daniel Mas, de la Vega, Ray Verma, Miriam Barrabes
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (538) Issue with article?

Generative Moment Matching Networks for Genotype Simulation

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By Daniel Mas Montserrat, Maria Perera, Miriam Barrabes, Margarita Geleta
| BioRxiv
Abstract The generation of synthetic genomic sequences using neural networks has potential to ameliorate privacy and data sharing concerns and to mitigate potential bias within datasets due to under-representation of some population groups. However, there is not a consensus on which architectures, training procedures, and evaluation metrics should be used when simulating single nucleotide polymorphism (SNP) sequences with neural networks.
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