Mahler Revsine - "Personal Transcriptome Variation Is Poorly Explained by Current Genomic Deep Learning Models" (Huang et al.); "Benchmarking of Deep Neural Networks for Predicting Personal Gene Expression from DNA Sequence Highlights Shortcomings" (Sasse et al.)
08 Apr 2025Overview
Paper Information
- Title: Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings
- Authors: Alexander Sasse, Bernard Ng, Anna E. Spiro, Shinya Tasaki, David A. Bennett, Christopher Gaiteri, Philip L. De Jager, Maria Chikina & Sara Mostafavi
- Journal: Nature Genetics volume 55, pages2060–2064 (2023)
- DOI & Link:https://www.nature.com/articles/s41588-023-01524-6
- Title: Personal transcriptome variation is poorly explained by current genomic deep learning models
- Authors: Connie Huang, Richard W. Shuai, Parth Baokar, Ryan Chung, Ruchir Rastogi, Pooja Kathail & Nilah M. Ioannidis
- Journal: Nature Genetics volume 55, pages2056–2059 (2023)
- DOI & Link:https://www.nature.com/articles/s41588-023-01574-w
Slides
Below are the slides for this talk, embedded directly from Google Drive:
Slides
Contact
For questions or further discussion, please reach out to genomicdeeplearning@cs.jhu.edu