Gamze Gürsoy
@gamzeandgursoy.bsky.social
Herbert Irving Assistant Professor of Biomedical Informatics at Columbia University; Core Faculty Member at New York Genome Center (she/her)
g2lab.org
g2lab.org
Many thanks to the MacMillan Center for the Study of the Non-coding Cancer Genome @nygenome for making this work possible, and @ColumbiaDBMI for always being so supportive of our work. (4/4)
April 14, 2025 at 5:02 PM
Many thanks to the MacMillan Center for the Study of the Non-coding Cancer Genome @nygenome for making this work possible, and @ColumbiaDBMI for always being so supportive of our work. (4/4)
We think this approach can have a lot of potential in downstream tasks such as high-resolution enhancer-gene linkages, hypothesis generation, annotating non-coding variants, and even perhaps for fine mapping (3/n)
April 14, 2025 at 5:02 PM
We think this approach can have a lot of potential in downstream tasks such as high-resolution enhancer-gene linkages, hypothesis generation, annotating non-coding variants, and even perhaps for fine mapping (3/n)
We developed a physical modeling based approach to "fill in the blanks" in the Hi-C matrices, which allows unsupervised imputation of high-resolution matrices without needing a high-resolution training dataset. (2/n)
April 14, 2025 at 5:02 PM
We developed a physical modeling based approach to "fill in the blanks" in the Hi-C matrices, which allows unsupervised imputation of high-resolution matrices without needing a high-resolution training dataset. (2/n)
that is homomorphic encryption friendly. It's complexity is independent of the number of reads. We think the optimizations in this algorithm can be used in many plaintext NGS processing algorithms too! 2/2
March 10, 2025 at 8:29 PM
that is homomorphic encryption friendly. It's complexity is independent of the number of reads. We think the optimizations in this algorithm can be used in many plaintext NGS processing algorithms too! 2/2
This metric allows us to determine (in a principled way) which NN layers would benefit from federation and which layers should be locally trained when data is non-IID. Please let us know if you have a study that can benefit from implementing this tool! (2/2)
February 15, 2025 at 9:57 PM
This metric allows us to determine (in a principled way) which NN layers would benefit from federation and which layers should be locally trained when data is non-IID. Please let us know if you have a study that can benefit from implementing this tool! (2/2)
Many thanks to @columbiauniversity.bsky.social and @nygenome.org - I cannot emphasize more that without resources, facilities, and admin support, none of the science is possible! (5/n)
February 12, 2025 at 4:17 PM
Many thanks to @columbiauniversity.bsky.social and @nygenome.org - I cannot emphasize more that without resources, facilities, and admin support, none of the science is possible! (5/n)
and many many thanks to @tuuliel.bsky.social for helping us greatly with the deep dive to our results, robustness, as well as making the paper readable for the audience we targeted. We are extremely lucky to be surrounded by great scientists from many disciplines! (4/n)
February 12, 2025 at 4:17 PM
and many many thanks to @tuuliel.bsky.social for helping us greatly with the deep dive to our results, robustness, as well as making the paper readable for the audience we targeted. We are extremely lucky to be surrounded by great scientists from many disciplines! (4/n)
This work wouldn't be possible without help from our great collaborators. Peihan helped tremendously to understand the theories behind MPC and its implementation (3/n)
February 12, 2025 at 4:17 PM
This work wouldn't be possible without help from our great collaborators. Peihan helped tremendously to understand the theories behind MPC and its implementation (3/n)
She developed clever algorithms to make eQTL mapping Multiparty Computation friendly and performed a very rigorous analysis of its accuracy, performance, and security. Many thanks to our visiting student Yebin for the assist as well. (2/n)
February 12, 2025 at 4:17 PM
She developed clever algorithms to make eQTL mapping Multiparty Computation friendly and performed a very rigorous analysis of its accuracy, performance, and security. Many thanks to our visiting student Yebin for the assist as well. (2/n)
Many thanks to the lead authors for bringing this idea to a different level and making this tool to scale to biobanks! We dedicate this paper to the late Dr. Lehner, whose visionary ideas inspired this work. His presence is deeply missed!
December 18, 2024 at 7:29 PM
Many thanks to the lead authors for bringing this idea to a different level and making this tool to scale to biobanks! We dedicate this paper to the late Dr. Lehner, whose visionary ideas inspired this work. His presence is deeply missed!