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Large pre-trained language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data.
Few-Shot Learning with Localization in Realistic Settings, July 2019
Davis Wertheimer and Bharath Hariharan · 1904
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The search for synergy: a critical review from a response surface perspective
W R Greco, G Bravo, and J C Parsons · 1995
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Deep Entity Matching with Pre-Trained Language Models
Yuliang Li, Jinfeng Li, Yoshihiko Suhara, AnHai Doan, and Wang-Chiew Tan · 2004
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Language Models are Few-Shot Learners, July 2020
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2005
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Clinical, pharmacokinetic (PK), and pharmacodynamic findings from a phase I trial of an eg5 inhibitor (AZD4877) in patients with refractory acute myeloid leukemia (AML)
G Borthakur, S Faderl, F Ravandi, S Padmanabhan, W Stock, K Wu, J Li, G Curt, M Tallman, and M Minden · 2009
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Overexpression of pim-1 in bladder cancer
Shengjie Guo, Xiaopeng Mao, Junxing Chen, Bin Huang, Chu Jin, Zhenbo Xu, and Shaopeng Qiu · 2010
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Similarities and Differences in the Expression of Drug-Metabolizing Enzymes between Human Hepatic Cell Lines and Primary Human Hepatocytes
Lei Guo, Stacey Dial, Leming Shi, William Branham, Jie Liu, Jia-Long Fang, Bridgett Green, Helen Deng, Jim Kaput, and Baitang Ning · 2011
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TabTransformer: Tabular Data Modeling Using Contextual Embeddings, December 2020
Xin Huang, Ashish Khetan, Milan Cvitkovic, and Zohar Karnin · 2012
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Phase II study to assess the efficacy, safety and tolerability of the mitotic spindle kinesin inhibitor AZD4877 in patients with recurrent advanced urothelial cancer
Robert Jones, Jacqueline Vuky, Tony Elliott, Graham Mead, José Angel Arranz, John Chester, Simon Chowdhury, Arkadiusz Z Dudek, Volker Müller-Mattheis, Marc-Oliver Grimm, Jürgen E Gschwend, Christian Wülfing, Peter Albers, Jianguo Li, Anna Osmukhina, Jeffrey Skolnik, and Gary Hudes · 2013
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Searching for Drug Synergy in Complex Dose-Response Landscapes Using an Interaction Potency Model
Bhagwan Yadav, Krister Wennerberg, Tero Aittokallio, and Jing Tang · 2015
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XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, July 2017
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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SynergyFinder: a web application for analyzing drug combination dose-response matrix data
Aleksandr Ianevski, Liye He, Tero Aittokallio, and Jing Tang · 2017
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Learning From Noisy Large-Scale Datasets With Minimal Supervision, April 2017
Andreas Veit, Neil Alldrin, Gal Chechik, Ivan Krasin, Abhinav Gupta, and Serge Belongie · 2017
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DrugCom: Synergistic Discovery of Drug Combinations Using Tensor Decomposition
Huiyuan Chen and Jing Li · 2018
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Phase I studies of AZD1208, a proviral integration moloney virus kinase inhibitor in solid and haematological cancers
Jorge Cortes, Kenji Tamura, Daniel J DeAngelo, Johann de Bono, David Lorente, Mark Minden, Geoffrey L Uy, Hagop Kantarjian, Lisa S Chen, Varsha Gandhi, Robert Godin, Karen Keating, Kristen McEachern, Karthick Vishwanathan, Janet Elizabeth Pease, and Emma Dean · 2018
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Methods for High-throughput Drug Combination Screening and Synergy Scoring
Liye He, Evgeny Kulesskiy, Jani Saarela, Laura Turunen, Krister Wennerberg, Tero Aittokallio, and Jing Tang · 2018
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Network Propagation Predicts Drug Synergy in Cancers
Hongyang Li, Tingyang Li, Daniel Quang, and Yuanfang Guan · 2018
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DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
Kristina Preuer, Richard P I Lewis, Sepp Hochreiter, Andreas Bender, Krishna C Bulusu, and Günter Klambauer · 2018
Cited alongside, same era.
Language Models are Unsupervised Multitask Learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2018
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Predicting Cancer Drug Response using a Recommender System
Chayaporn Suphavilai, Denis Bertrand, and Niranjan Nagarajan · 2018
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In-silico prediction of synergistic Anti-Cancer drug combinations using multi-omics data
Remzi Celebi, Oliver Bear Don’t Walk, 4th, Rajiv Movva, Semih Alpsoy, and Michel Dumontier · 2019
Cited alongside, same era.
Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction
Xuan Liu, Congzhi Song, Shichao Liu, Menglu Li, Xionghui Zhou, and Wen Zhang · 2022
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Can Foundation Models Wrangle Your Data?, December 2022
Avanika Narayan, Ines Chami, Laurel Orr, Simran Arora, and Christopher Ré · 2022
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Introducing chatgpt, 2022
OpenAI · 2022
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Large pre-trained language models contain human-like biases of what is right and wrong to do
Patrick Schramowski, Cigdem Turan, Nico Andersen, Constantin A Rothkopf, and Kristian Kersting · 2022
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SynPathy: Predicting drug synergy through Drug-Associated pathways using deep learning
Yi-Ching Tang and Assaf Gottlieb · 2022
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Fabiola Cervantes-Gomez, Christine M Stellrecht, Mary L Ayres, Michael J Keating, William G Wierda, and Varsha Gandhi · 2019
Cited alongside, same era.
Network-based prediction of drug combinations
Feixiong Cheng, István A Kovács, and Albert-László Barabási · 2019
Cited alongside, same era.
Predicting synergism of cancer drug combinations using NCI-ALMANAC data
Pavel Sidorov, Stefan Naulaerts, Jérémy Ariey-Bonnet, Eddy Pasquier, and Pedro J Ballester · 2019
Cited alongside, same era.
DrugComb: an integrative cancer drug combination data portal
Bulat Zagidullin, Jehad Aldahdooh, Shuyu Zheng, Wenyu Wang, Yinyin Wang, Joseph Saad, Alina Malyutina, Mohieddin Jafari, Ziaurrehman Tanoli, Alberto Pessia, and Jing Tang · 2019
Cited alongside, same era.
The Bone Extracellular Matrix in Bone Formation and Regeneration
Xiao Lin, Suryaji Patil, Yong-Guang Gao, and Airong Qian · 2020
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DTF: Deep Tensor Factorization for predicting anticancer drug synergy
Zexuan Sun, Shujun Huang, Peiran Jiang, and Pingzhao Hu · 2020
Cited alongside, same era.
Huggingface’s transformers: State-of-the-art natural language processing, 2020
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
Cited alongside, same era.
Automatic strain sensor design via active learning and data augmentation for soft machines
Haitao Yang, Jiali Li, Kai Zhuo Lim, Chuanji Pan, Tien Van Truong, Qian Wang, Kerui Li, Shuo Li, Xiao Xiao, Meng Ding, Tianle Chen, Xiaoli Liu, Qian Xie, Pablo Valdivia y Alvarado, Xiaonan Wang, and Po-Yen Chen · 2022
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Pan-Peptide meta learning for t-cell receptor–antigen binding recognition
Yicheng Gao, Yuli Gao, Yuxiao Fan, Chengyu Zhu, Zhiting Wei, Chi Zhou, Guohui Chuai, Qinchang Chen, He Zhang, and Qi Liu · 2023
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TabLLM: Few-shot Classification of Tabular Data with Large Language Models, March 2023
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag · 2023
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CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy
Sayed-Rzgar Hosseini and Xiaobo Zhou · 2023
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Large language models generate functional protein sequences across diverse families
Ali Madani, Ben Krause, Eric R Greene, Subu Subramanian, Benjamin P Mohr, James M Holton, Jose Luis Olmos, Jr, Caiming Xiong, Zachary Z Sun, Richard Socher, James S Fraser, and Nikhil Naik · 2023
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The debate over understanding in AI’s large language models
Melanie Mitchell and David C Krakauer · 2023
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Foundation models for generalist medical artificial intelligence
Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad, Harlan M Krumholz, Jure Leskovec, Eric J Topol, and Pranav Rajpurkar · 2023
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Stunt: Few-shot tabular learning with self-generated tasks from unlabeled tables
Jaehyun Nam, Jihoon Tack, Kyungmin Lee, Hankook Lee, and Jinwoo Shin · 2023
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NCI drug dictionary
National Cancer Institute · 2023
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NVIDIA BioNeMo cloud service: An end-to-end AI-powered drug discovery pipelines
NVIDIA · 2023
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GPT-4 technical report
OpenAI · 2023
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Meta-learning for T cell receptor binding specificity and beyond
Duolin Wang, Fei He, Yang Yu, and Dong Xu · 2023
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