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Machine learning has emerged as a powerful tool for scientific discovery, enabling researchers to extract meaningful insights from complex datasets.
Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
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Gene expression profiling predicts clinical outcome of breast cancer
L. J. Van’t Veer, H. Dai, M. J. Van De Vijver, Y. D. He, A. A. Hart, M. Mao, H. L. Peterse, K. Van Der Kooy, M. J. Marton, A. T. Witteveen, et al · 2002
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Classification and selection of biomarkers in genomic data using lasso
D. Ghosh and A. M. Chinnaiyan · 2005
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Statistical methods for pre-processing microarray gene expression data
M. M. R. Khondoker · 2006
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A unified mixed-model method for association mapping that accounts for multiple levels of relatedness
J. Yu, G. Pressoir, W. H. Briggs, I. Vroh Bi, M. Yamasaki, J. F. Doebley, M. D. McMullen, B. S. Gaut, D. M. Nielsen, J. B. Holland, et al · 2006
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The human solute carrier family 11 member 1 protein (slc11a1): linking infections, autoimmunity and cancer?
A. A. Awomoyi · 2007
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Individualization of therapy using mammaprint® ì: from development to the mindact trial
S. Mook, L. J. Van’t Veer, E. J. Rutgers, M. J. Piccart-Gebhart, and F. Cardoso · 2007
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Genome-wide association analysis by lasso penalized logistic regression
T. T. Wu, Y. F. Chen, T. Hastie, E. Sobel, and K. Lange · 2009
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Vitamin d: modulator of the immune system
F. Baeke, T. Takiishi, H. Korf, C. Gysemans, and C. Mathieu · 2010
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The path to personalized medicine
M. A. Hamburg and F. S. Collins · 2010
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Tackling the widespread and critical impact of batch effects in high-throughput data
J. T. Leek, R. B. Scharpf, H. C. Bravo, D. Simcha, B. Langmead, W. E. Johnson, D. Geman, K. Baggerly, and R. A. Irizarry · 2010
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Personalized medicine: progress and promise
I. S. Chan and G. S. Ginsburg · 2011
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Fast linear mixed models for genome-wide association studies
C. Lippert, J. Listgarten, Y. Liu, C. M. Kadie, R. I. Davidson, and D. Heckerman · 2011
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A comparative study of feature selection and classification methods for gene expression data of glioma
H. Abusamra · 2013
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The cancer genome atlas (tcga): an immeasurable source of knowledge
K. Tomczak, P. Czerwińska, and M. Wiznerowicz · 2014
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Gene: a gene-centered information resource at ncbi
G. R. Brown, V. Hem, K. S. Katz, M. Ovetsky, C. Wallin, O. Ermolaeva, I. Tolstoy, T. Tatusova, K. D. Pruitt, D. R. Maglott, et al · 2015
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Confounding factors in the transcriptome analysis of an in-vivo exposure experiment
O. Bruning, W. Rodenburg, P. F. Wackers, C. Van Oostrom, M. J. Jonker, R. J. Dekker, H. Rauwerda, W. A. Ensink, A. De Vries, and T. M. Breit · 2016
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The gene expression omnibus database
E. Clough and T. Barrett · 2016
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Modulation of inflammatory and immune responses by vitamin d
F. Colotta, B. Jansson, and F. Bonelli · 2017
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Mammaprint™: a comprehensive review
M. Brandão, N. Pondé, and M. Piccart-Gebhart · 2019
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Socs1 and its potential clinical role in tumor
J. Ying, X. Qiu, Y. Lu, and M. Zhang · 2019
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Visualizing and interpreting cancer genomics data via the xena platform
M. J. Goldman, B. Craft, M. Hastie, K. Repečka, F. McDade, A. Kamath, A. Banerjee, Y. Luo, D. Rogers, A. N. Brooks, et al · 2020
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From big data to better patient outcomes
T. Hulsen, D. Friedecký, H. Renz, E. Melis, P. Vermeersch, and P. Fernandez-Calle · 2022
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Chain-of-thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al · 2022
Communicative agents for software development
C. Qian, X. Cong, W. Liu, C. Yang, W. Chen, Y. Su, Y. Dang, J. Li, J. Xu, D. Li, Z. Liu, and M. Sun · 2023
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Novel precision medicine approaches and treatment strategies in hematological malignancies
R. Rosenquist, E. Bernard, T. Erkers, D. W. Scott, R. Itzykson, P. Rousselot, J. Soulier, M. Hutchings, P. Östling, L. Cavelier, et al · 2023
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Multi-agent collaboration: Harnessing the power of intelligent llm agents
Y. Talebirad and A. Nadiri · 2023
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Synapse: Leveraging few-shot exemplars for human-level computer control
L. Zheng, R. Wang, and B. An · 2023
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Llmarena: Assessing capabilities of large language models in dynamic multi-agent environments
J. Chen, X. Hu, S. Liu, S. Huang, W.-W. Tu, Z. He, and L. Wen · 2024
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React: Synergizing reasoning and acting in language models
S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao · 2022
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Chemcrow: Augmenting large-language models with chemistry tools
A. M. Bran, S. Cox, O. Schilter, C. Baldassari, A. D. White, and P. Schwaller · 2023
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Self-collaboration code generation via chatgpt
Y. Dong, X. Jiang, Z. Jin, and G. Li · 2023
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Improving factuality and reasoning in language models through multiagent debate
Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch · 2023
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Towards revealing the mystery behind chain of thought: A theoretical perspective
G. Feng, B. Zhang, Y. Gu, H. Ye, D. He, and L. Wang · 2023
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What can large language models do in chemistry? a comprehensive benchmark on eight tasks
T. Guo, K. Guo, B. Nan, Z. Liang, Z. Guo, N. V. Chawla, O. Wiest, and X. Zhang · 2023
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Agentquest: A modular benchmark framework to measure progress and improve llm agents
L. Gioacchini, G. Siracusano, D. Sanvito, K. Gashteovski, D. Friede, R. Bifulco, and C. Lawrence · 2024
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Forecasting high-impact research topics via machine learning on evolving knowledge graphs
X. Gu and M. Krenn · 2024
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Large language model based multi-agents: A survey of progress and challenges, 2024
T. Guo, X. Chen, Y. Wang, R. Chang, S. Pei, N. V. Chawla, O. Wiest, and X. Zhang · 2024
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The ai scientist: Towards fully automated open-ended scientific discovery
C. Lu, C. Lu, R. T. Lange, J. Foerster, J. Clune, and D. Ha · 2024
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A comparison of rna-seq data preprocessing pipelines for transcriptomic predictions across independent studies
T. Mize et al · 2024
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A review of current trends, techniques, and challenges in large language models (llms)
R. Patil and V. Gudivada · 2024
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Chatnt: A multimodal conversational agent for dna, rna and protein tasks
G. Richard, B. P. de Almeida, H. Dalla-Torre, C. Blum, L. Hexemer, P. Pandey, S. Laurent, M. Lopez, A. Laterre, M. Lang, et al · 2024
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A. Saadat and J. Fellay · 2024
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Mimir: A customizable agent tuning platform for enhanced scientific applications
X. Tang, C. Deng, H. Hanminwang, H. Wang, Y. Zhao, W. Shi, Y. Fung, W. Zhou, J. Cao, H. Ji, et al · 2024
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Deep learning in cancer genomics and histopathology
M. Unger and J. N. Kather · 2024
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W. Wang, D. Zhang, T. Feng, B. Wang, and J. Tang · 2024
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Agent-safetybench: Evaluating the safety of llm agents
Z. Zhang, S. Cui, Y. Lu, J. Zhou, J. Yang, H. Wang, and M. Huang · 2024
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