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Jillian Parker on Muck Rack

Jillian Parker

Rochester
As seen in: Medium, Nature, SAGE Publishing, ACS Publications, BioRxiv, Cell Press, Spectrum News Rochester, Spectrum News 13, KSFY-TV (Sioux Falls, SD), Bay News 9 and
| Former Morning Reporter (still catching up on sleep) | Healthcare Public Relations | Jersey Raised | Yankees | Tweets are my own |
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Articles

A genome-scale CRISPRi perturbation atlas of human induced pluripotent stem cells

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By Sami Nourreddine, Yesh Doctor, Amir Dailamy, Jan N. Hansen, Antoine Forget, Benjamin J. Polacco, Monita Muralidharan, Alina Sigaeva, Emily Pan, Jiahao Gao, Jake Chen, Timothy D. Clark, Jillian Parker, Kirsten Obernier, Christian M. Metallo, Trey Ideker, Emma Lundberg, Nevan J. Krogan, Prashant Mali, Yi-Hung Lee, Rebecca Chinn, Sushant Sunder
| Nature Verified
Abstract Comprehensively mapping the relationship between genotype and phenotype offers essential insights into how a cell’s state arises from its genetic components. Toward this goal, we generated an expressed genome-scale CRISPRi perturbation cell atlas in KOLF2.1J human induced pluripotent stem cells, mapping transcriptional phenotypes associated with 11,692 perturbed genes across >2.5 million single cells.
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Cat

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By Jillian Parker
| Medium
I don’t even have a name. You let me waste away in this empty apartment with buckets of champagne. You sleep through the day and wake in a haze from dreaming about your life. You could have had it your way. I sift through the crowds. Music entirely too loud. A fly on the wall. Watching you bat away their hands. But I’ve been here all along. The first to hear your songs about the moon and the trees and the river that’s so long. So pull down your mask close your eyes again.
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AI-readiness for Biomedical Data: Bridge2AI Recommendations

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By Timothy Clark, Harry Caufield, Jillian Parker, Sadnan Al Manir
| BioRxiv
Abstract Biomedical research and clinical practice are in the midst of a transition toward significantly increased use of artificial intelligence (AI) and machine learning (ML) methods. These advances promise to enable qualitatively deeper insight into complex challenges formerly beyond the reach of analytic methods and human intuition while placing increased demands on ethical and explainable artificial intelligence (XAI), given the opaque nature of many deep learning methods.
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