Traineeship in Advanced Data Analysis (AI and Machine Learning)
I was selected to receive the AIM-AHEAD (Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) and NIH National Center For Advancing Translational Science (NCATS) Training Program. This is an intensive 8-month program in advanced data analysis developed by National Center for Advancing Translational Sciences (NCATS) and utilizing the resources of the NCATS N3C Data Enclave and AIM-AHEAD’s data science training core. Completing the training will equip the motivated professional to conduct the in-depth analysis of large datasets essential for cutting-edge biomedical and socioeconomic research. Check it out
Jan 25, 2024
Reproductive genomics of the mouse: implications for human fertility and infertility
Genetic analyses of mammalian gametogenesis and fertility have the potential to inform about two important and interrelated clinical areas: infertility and contraception. Here, we address the genetics and genomics underlying gamete formation, productivity and function in the context of reproductive success in mammalian systems, primarily mouse and human.
Feb 13, 2023
Taxonomic assessment of two wild house mouse subspecies using whole-genome sequencing
Overall, our work suggests that the M. m. bactrianus and M. m. helgolandicus subspecies are not well-justified taxonomic entities, emphasizing the importance of leveraging whole-genome sequence data to inform subspecies designations. Additionally, our investigation provides tailored experimental procedures for generating whole genome sequences from air-dried mouse skins, along with key genomic resources to inform future genomic studies of wild mouse diversity.
Nov 8, 2022
Mutation Rates
May 8, 2020
Infertility and Reproduction
May 8, 2020
Evolutionary Genomics
May 8, 2020
Genome-scale data reveal the role of hybridization in lichen forming fungi
We discuss the potential roles of hybridization in terms of generating asexuality and novel morphological traits in lichens. Our results highlight the need for additional study of reticulate phylogenies when investigating species boundaries and evolutionary history, even in cases with well-supported topologies inferred from genome-scale data.
Jan 8, 2020