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Nilah M Ioannidis

Showing results (1-10 of 24) with videos related to

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Biorxiv : the Preprint Server for Biology|October 31, 2023
GUANinE v1.0: Benchmark Datasets for Genomic AI Sequence-to-Function ModelsEyes S Robson, Nilah M Ioannidis
Bioinformatics (Oxford, England)|April 25, 2020
Predicting target genes of non-coding regulatory variants with IRTZhenqin Wu, Nilah M Ioannidis, James Zou
Genome Biology|May 26, 2026
Fine-tuning sequence-to-expression models on personal genome and transcriptome dataRuchir Rastogi, Aniketh Janardhan Reddy, Ryan Chung, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 27, 2026
Session Introduction: Precision Medicine: Integrating Large-Scale Data and Intermediate Phenotypes for Understanding Health and Treating DiseaseSteven E Brenner, Nilah M Ioannidis, Tayo Obafemi-Ajayi, et al.
Biorxiv : the Preprint Server for Biology|January 8, 2024
Characterizing uncertainty in predictions of genomic sequence-to-activity modelsAyesha Bajwa, Ruchir Rastogi, Pooja Kathail, et al.
The Journal of Investigative Dermatology|November 27, 2018
A Prediction Tool to Facilitate Risk-Stratified Screening for Squamous Cell Skin CancerWei Wang, Eric Jorgenson, Nilah M Ioannidis, et al.
Human Immunology|February 11, 2017
Cutaneous squamous cell cancer (cSCC) risk and the human leukocyte antigen (HLA) systemPooja Yesantharao, Wei Wang, Nilah M Ioannidis, et al.
Nature Genetics|November 30, 2023
Personal transcriptome variation is poorly explained by current genomic deep learning modelsConnie Huang, Richard W Shuai, Parth Baokar, et al.
Genome Biology|August 1, 2024
Current genomic deep learning models display decreased performance in cell type-specific accessible regionsPooja Kathail, Richard W Shuai, Ryan Chung, et al.
Biorxiv : the Preprint Server for Biology|July 19, 2024
Current genomic deep learning models display decreased performance in cell type specific accessible regionsPooja Kathail, Richard W Shuai, Ryan Chung, et al.
Pageof 3

Showing results (1-10 of 24) with videos related to

Sort By:
Pageof 3
Biorxiv : the Preprint Server for Biology|October 31, 2023
GUANinE v1.0: Benchmark Datasets for Genomic AI Sequence-to-Function ModelsEyes S Robson, Nilah M Ioannidis
Bioinformatics (Oxford, England)|April 25, 2020
Predicting target genes of non-coding regulatory variants with IRTZhenqin Wu, Nilah M Ioannidis, James Zou
Genome Biology|May 26, 2026
Fine-tuning sequence-to-expression models on personal genome and transcriptome dataRuchir Rastogi, Aniketh Janardhan Reddy, Ryan Chung, et al.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|February 27, 2026
Session Introduction: Precision Medicine: Integrating Large-Scale Data and Intermediate Phenotypes for Understanding Health and Treating DiseaseSteven E Brenner, Nilah M Ioannidis, Tayo Obafemi-Ajayi, et al.
Biorxiv : the Preprint Server for Biology|January 8, 2024
Characterizing uncertainty in predictions of genomic sequence-to-activity modelsAyesha Bajwa, Ruchir Rastogi, Pooja Kathail, et al.
The Journal of Investigative Dermatology|November 27, 2018
A Prediction Tool to Facilitate Risk-Stratified Screening for Squamous Cell Skin CancerWei Wang, Eric Jorgenson, Nilah M Ioannidis, et al.
Human Immunology|February 11, 2017
Cutaneous squamous cell cancer (cSCC) risk and the human leukocyte antigen (HLA) systemPooja Yesantharao, Wei Wang, Nilah M Ioannidis, et al.
Nature Genetics|November 30, 2023
Personal transcriptome variation is poorly explained by current genomic deep learning modelsConnie Huang, Richard W Shuai, Parth Baokar, et al.
Genome Biology|August 1, 2024
Current genomic deep learning models display decreased performance in cell type-specific accessible regionsPooja Kathail, Richard W Shuai, Ryan Chung, et al.
Biorxiv : the Preprint Server for Biology|July 19, 2024
Current genomic deep learning models display decreased performance in cell type specific accessible regionsPooja Kathail, Richard W Shuai, Ryan Chung, et al.
Pageof 3