The PRIMED Consortium: Reducing disparities in polygenic risk assessment

Autor: Adebamowo, Sally, Adebamowo, Clement, Allred, Nicholette, Auer, Paul, Below, Jennifer, Boua, Palwende Romuald, Boulier, Kristin, Bowers, Michael, Breeyear, Joseph, Chatterjee, Nilanjan, Chikowore, Tinashe, Choi, Jaewon, Choudhury, Ananyo, Conomos, Matthew, Conti, David, Cox, Nancy, Cullina, Sinead, Darst, Burcu, Deutsch, Aaron, Ding, Yi, Edwards, Todd, Eskin, Eleazar, Fatumo, Segun, Florez, Jose, Freimer, Nelson, Fullerton, Stephanie, Ge, Tian, Geschwind, Daniel, Gignoux, Chris, Gogarten, Stephanie, Goodarzi, Mark, Guo, Xiuqing, Haiman, Christopher, Hanchard, Neil, Hazelhurst, Scott, Heavner, Ben, Heckbert, Susan, Hirbo, Jibril, Hornsby, Whitney, Hou, Kangcheng, Huang, Qinqin, Huerta, Alicia, Jiang, Guoqian, Johnston, Katherine, Kachuri, Linda, Kadowaki, Takashi, Kamiza, Abram Bunya, Kenny, Eimear, Kerns, Sarah, Khan, Alyna, Kim, Joohyun, Konigsberg, Iain, Kooperberg, Charles, Kosel, Matt, Kraft, Peter, Kullo, Iftikhar, Kwak, Soo-Heon, Lange, Leslie, Lange, Ethan, Le Marchand, Loic, Lee, Hyunsuk, Leong, Aaron, Li, Yun, Lin, Meng, Lohmueller, Kirk, Loos, Ruth, Lu, Kevin, Mandia, Ravi, Manning, Alisa, Martin, Alicia, Martin, Iman, Martin, Hilary, Mathias, Rasika, Meigs, James, Mercader, Josep, Mester, Rachel, Meyer, Mariah, Miller-Fleming, Tyne, Mitchell, Braxton, Mulder, Nicola, Na, Jie, Natarajan, Pradeep, Nelson, Sarah, Ng, Maggie, Norland, Kristjan, Loohuis, Loes Olde, Onengut-Gumuscu, Suna, Oneyobi, Ebuka, Ophoff, Roel, Pajukanta, Paivi, Pasaniuc, Bogdan, Patel, Aniruddh, Peters, Ulrike, Phuong, Jimmy, Preuss, Michael, Psaty, Bruce, Raffield, Laura, Ramsay, Michele, Reiner, Alexander, Rice, Kenneth, Rich, Stephen, Rotter, Jerome, Rowan, Bryce, Rowley, Robb, Ruan, Yunfeng, Sakoda, Lori, Sankararaman, Siram, Schaid, Dan, Schrider, Dan, Schroeder, Philip, Shemirani, Ruhoilah, Shortt, Jonathan, Shuey, Megan, Sim, Xueling, Smit, Roelof A.J., Smith, Johanna, Sobrin, Lucia, Stalbow, Lauren, Stilp, Adrienne, Stram, Daniel, Suzuki, Ken, Szczerbinski, Lukasz, Tao, Ran, Tayo, Bamidele, Thornton, Timothy, Truong, Buu, Tusie, Teresa, Udler, Miriam, van Heel, David, Vargas, Luciana B., Venkateswaran, Vidhya, Wang, Ying, Wessel, Jennifer, Wiley, Laura, Wilkens, Lynne, Wilson, Riley, Witte, John, Wojcik, Genevieve, Wong, Quenna, Yamauchi, Toshimasa, Yanek, Lisa, Yu, Yue, Zhang, Haoyu, Zhang, Yuji, Zhong, Michael, Kullo, Iftikhar J., Conomos, Matthew P., Nelson, Sarah C., Adebamowo, Sally N., Fullerton, Stephanie M., Gogarten, Stephanie M., Hornsby, Whitney E., Kenny, Eimear E., Khera, Amit V., Mercader, Josep M., Raffield, Laura M., Reiner, Alex, Schaid, Daniel, Wiley, Ken, Witte, John S.

Publikováno v: In The American Journal of Human Genetics 5 December 2024 111(12):2594-2606

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Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

Autor: Du, Simon S., Hou, Kangcheng, Póczos, Barnabás, Salakhutdinov, Ruslan, Wang, Ruosong, Xu, Keyulu

While graph kernels (GKs) are easy to train and enjoy provable theoretical guarantees, their practical performances are limited by their expressive power, as the kernel function often depends on hand-crafted combinatorial features of graphs. Compared

Externí odkaz: http://arxiv.org/abs/1905.13192

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