BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery

Autor: John, Peter St., Lin, Dejun, Binder, Polina, Greaves, Malcolm, Shah, Vega, John, John St., Lange, Adrian, Hsu, Patrick, Illango, Rajesh, Ramanathan, Arvind, Anandkumar, Anima, Brookes, David H, Busia, Akosua, Mahajan, Abhishaike, Malina, Stephen, Prasad, Neha, Sinai, Sam, Edwards, Lindsay, Gaudelet, Thomas, Regep, Cristian, Steinegger, Martin, Rost, Burkhard, Brace, Alexander, Hippe, Kyle, Naef, Luca, Kamata, Keisuke, Armstrong, George, Boyd, Kevin, Cao, Zhonglin, Chou, Han-Yi, Chu, Simon, Costa, Allan dos Santos, Darabi, Sajad, Dawson, Eric, Didi, Kieran, Fu, Cong, Geiger, Mario, Gill, Michelle, Hsu, Darren, Kaushik, Gagan, Korshunova, Maria, Kothen-Hill, Steven, Lee, Youhan, Liu, Meng, Livne, Micha, McClure, Zachary, Mitchell, Jonathan, Moradzadeh, Alireza, Mosafi, Ohad, Nashed, Youssef, Paliwal, Saee, Peng, Yuxing, Rabhi, Sara, Ramezanghorbani, Farhad, Reidenbach, Danny, Ricketts, Camir, Roland, Brian, Shah, Kushal, Shimko, Tyler, Sirelkhatim, Hassan, Srinivasan, Savitha, Stern, Abraham C, Toczydlowska, Dorota, Veccham, Srimukh Prasad, Venanzi, Niccolò Alberto Elia, Vorontsov, Anton, Wilber, Jared, Wilkinson, Isabel, Wong, Wei Jing, Xue, Eva, Ye, Cory, Yu, Xin, Zhang, Yang, Zhou, Guoqing, Zandstein, Becca, Dallago, Christian, Trentini, Bruno, Kucukbenli, Emine, Rvachov, Timur, Calleja, Eddie, Israeli, Johnny, Clifford, Harry, Haukioja, Risto, Haemel, Nicholas, Tretina, Kyle, Tadimeti, Neha, Costa, Anthony B

Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language mode

Externí odkaz: http://arxiv.org/abs/2411.10548

CLT for single functional index quantile regression under dependence structure

Autor: Kadiri Nadia, Rabhi Abbes, Khardani Salah, Akkal Fatima

Publikováno v: Acta Universitatis Sapientiae: Mathematica, Vol 13, Iss 1, Pp 45-77 (2021)

In this paper, we investigate the asymptotic properties of a nonparametric conditional quantile estimation in the single functional index model for dependent functional data and censored at random responses are observed. First of all, we establish as

Externí odkaz: https://doaj.org/article/65947721edd944a5a606cdae976fcaea

Constraining the high-density behavior of nuclear symmetry energy with direct Urca processes

Autor: Boukari, Olfa, Malik, Tuhin, Rabhi, Aziz, Providência, Constança

The density dependence of the symmetry energy in relativistic mean-field models with density dependent couplings is discussed in terms of the possible opening of nucleonic direct Urca processes inside neutron stars, which induce a very rapid cooling

Externí odkaz: http://arxiv.org/abs/2407.04403

Strong uniform consistency rates of conditional quantile estimation in the single functional index model under random censorship

Autor: Kadiri Nadia, Rabhi Abbes, Bouchentouf Amina Angelika

Publikováno v: Dependence Modeling, Vol 6, Iss 1, Pp 197-227 (2018)

The main objective of this paper is to non-parametrically estimate the quantiles of a conditional distribution in the censorship model when the sample is considered as an -mixing sequence. First of all, a kernel type estimator for the conditional cum

Externí odkaz: https://doaj.org/article/6c80e2074193415fab4160ad0f0b97bb

LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking

Autor: Yue, Zhenrui, Rabhi, Sara, Moreira, Gabriel de Souza Pereira, Wang, Dong, Oldridge, Even

Recently, large language models (LLMs) have exhibited significant progress in language understanding and generation. By leveraging textual features, customized LLMs are also applied for recommendation and demonstrate improvements across diverse recom

Externí odkaz: http://arxiv.org/abs/2311.02089