Zobrazeno 1 - 10
of 1 149
pro vyhledávání: '"Kellis, Manolis"'
Protein function prediction is a crucial task in bioinformatics, with significant implications for understanding biological processes and disease mechanisms. While the relationship between sequence and function has been extensively explored, translat
Externí odkaz:
http://arxiv.org/abs/2409.00610
The rapid advancement of single-cell ATAC sequencing (scATAC-seq) technologies holds great promise for investigating the heterogeneity of epigenetic landscapes at the cellular level. The amplification process in scATAC-seq experiments often introduce
Externí odkaz:
http://arxiv.org/abs/2408.14801
Autor:
Huang, Yue, Sun, Lichao, Wang, Haoran, Wu, Siyuan, Zhang, Qihui, Li, Yuan, Gao, Chujie, Huang, Yixin, Lyu, Wenhan, Zhang, Yixuan, Li, Xiner, Liu, Zhengliang, Liu, Yixin, Wang, Yijue, Zhang, Zhikun, Vidgen, Bertie, Kailkhura, Bhavya, Xiong, Caiming, Xiao, Chaowei, Li, Chunyuan, Xing, Eric, Huang, Furong, Liu, Hao, Ji, Heng, Wang, Hongyi, Zhang, Huan, Yao, Huaxiu, Kellis, Manolis, Zitnik, Marinka, Jiang, Meng, Bansal, Mohit, Zou, James, Pei, Jian, Liu, Jian, Gao, Jianfeng, Han, Jiawei, Zhao, Jieyu, Tang, Jiliang, Wang, Jindong, Vanschoren, Joaquin, Mitchell, John, Shu, Kai, Xu, Kaidi, Chang, Kai-Wei, He, Lifang, Huang, Lifu, Backes, Michael, Gong, Neil Zhenqiang, Yu, Philip S., Chen, Pin-Yu, Gu, Quanquan, Xu, Ran, Ying, Rex, Ji, Shuiwang, Jana, Suman, Chen, Tianlong, Liu, Tianming, Zhou, Tianyi, Wang, William, Li, Xiang, Zhang, Xiangliang, Wang, Xiao, Xie, Xing, Chen, Xun, Wang, Xuyu, Liu, Yan, Ye, Yanfang, Cao, Yinzhi, Chen, Yong, Zhao, Yue
Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Ther
Externí odkaz:
http://arxiv.org/abs/2401.05561
We examine Contextualized Machine Learning (ML), a paradigm for learning heterogeneous and context-dependent effects. Contextualized ML estimates heterogeneous functions by applying deep learning to the meta-relationship between contextual informatio
Externí odkaz:
http://arxiv.org/abs/2310.11340
Autor:
Lengerich, Benjamin J., Bordt, Sebastian, Nori, Harsha, Nunnally, Mark E., Aphinyanaphongs, Yin, Kellis, Manolis, Caruana, Rich
We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components. By adopting a hierarchical approach to reasoning, LLMs can provide compre
Externí odkaz:
http://arxiv.org/abs/2308.01157
Autor:
Agarwal, Manan, Alameda, Jay, Audenaert, Jeroen, Benoit, Will, Beveridge, Damon, Bhattacharya, Meghna, Chatterjee, Chayan, Chatterjee, Deep, Chen, Andy, Cholayil, Muhammed Saleem, Chou, Chia-Jui, Choudhary, Sunil, Coughlin, Michael, Dax, Maximilian, Desai, Aman, Di Luca, Andrea, Duarte, Javier Mauricio, Farrell, Steven, Feng, Yongbin, Goodarzi, Pooyan, Govorkova, Ekaterina, Graham, Matthew, Guiang, Jonathan, Gunny, Alec, Guo, Weichangfeng, Hakenmueller, Janina, Hawks, Ben, Hsu, Shih-Chieh, Jawahar, Pratik, Ju, Xiangyang, Katsavounidis, Erik, Kellis, Manolis, Khoda, Elham E, Lahbabi, Fatima Zahra, Lian, Van Tha Bik, Liu, Mia, Malanchev, Konstantin, Marx, Ethan, McCormack, William Patrick, McLeod, Alistair, Mo, Geoffrey, Moreno, Eric Anton, Muthukrishna, Daniel, Narayan, Gautham, Naylor, Andrew, Neubauer, Mark, Norman, Michael, Omer, Rafia, Pedro, Kevin, Peterson, Joshua, Pürrer, Michael, Raikman, Ryan, Raj, Shivam, Ricker, George, Robbins, Jared, Samani, Batool Safarzadeh, Scholberg, Kate, Schuy, Alex, Skliris, Vasileios, Soni, Siddharth, Sravan, Niharika, Sutton, Patrick, Villar, Victoria Ashley, Wang, Xiwei, Wen, Linqing, Wuerthwein, Frank, Yang, Tingjun, Yeh, Shu-Wei
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient w
Externí odkaz:
http://arxiv.org/abs/2306.08106
Autor:
Kellis, Manolis, D’Agostino, John
Publikováno v:
Horizons: Journal of International Relations and Sustainable Development, 2023 Jul 01(24), 46-61.
Externí odkaz:
https://www.jstor.org/stable/48761162
Autor:
Whited, A.M., Jungreis, Irwin, Allen, Jeffre, Cleveland, Christina L., Mudge, Jonathan M., Kellis, Manolis, Rinn, John L., Hough, Loren E.
Publikováno v:
In Biophysical Reports 11 September 2024 4(3)
Autor:
Li, Chengyu, Chen, Kexuan, Fang, Qianchen, Shi, Shaohui, Nan, Jiuhong, He, Jialin, Yin, Yafei, Li, Xiaoyu, Li, Jingyun, Hou, Lei, Hu, Xinyang, Kellis, Manolis, Han, Xikun, Xiong, Xushen
Publikováno v:
In Cell Genomics 14 August 2024 4(8)
Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) identify context-dependent relationships between variables, but the non-convexity induced by the acyclicity requirement makes it difficult to share information between context-sp
Externí odkaz:
http://arxiv.org/abs/2111.01104