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

SecDOAR: A Software Reference Architecture for Security Data Orchestration, Analysis and Reporting

Autor: Chauhan, Muhammad Aufeef, Babar, Muhammad Ali, Rabhi, Fethi

A Software Reference Architecture (SRA) is a useful tool for standardising existing architectures in a specific domain and facilitating concrete architecture design, development and evaluation by instantiating SRA and using SRA as a benchmark for the

Externí odkaz: http://arxiv.org/abs/2408.12904

SLEGO: A Collaborative Data Analytics System with LLM Recommender for Diverse Users

Autor: Ng, Siu Lung, Rezaei, Hirad Baradaran, Rabhi, Fethi

This paper presents the SLEGO (Software-Lego) system, a collaborative analytics platform that bridges the gap between experienced developers and novice users using a cloud-based platform with modular, reusable microservices. These microservices enabl

Externí odkaz: http://arxiv.org/abs/2406.11232

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

Digital transformation metamodel in smart farming: Crop classification prediction based on recurrent neural network

Autor: Loubna Rabhi, Brahim Jabir, Noureddine Falih, Lekbir Afraites, Belaid Bouikhalene

Publikováno v: Foods and Raw Materials, Vol 13, Iss 1, Pp 107-118 (2025)

Agriculture 4.0 is an opportunity for farmers to meet the current challenges in food production. It has become necessary to adopt a set of agricultural practices based on advanced technologies. Agriculture 4.0 enables farms to create added value by c

Externí odkaz: https://doaj.org/article/d8d1353cf37f427fb8a5dd1dec3dc4e8