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We envision "AI scientists" as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms.
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Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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A tutorial on conducting genome-wide association studies: Quality control and statistical analysis
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Gwas meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture
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Deep learning enables rapid identification of potent ddr1 kinase inhibitors
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Unsupervised word embeddings capture latent knowledge from materials science literature
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Spatial proteomics: a powerful discovery tool for cell biology
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Mitochondrial fragmentation drives selective removal of deleterious mtdna in the germline
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A robotic platform for flow synthesis of organic compounds informed by ai planning
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Predicting drug response and synergy using a deep learning model of human cancer cells
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A deep learning approach to antibiotic discovery
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Language models are few-shot learners
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Live-imaging of astrocyte morphogenesis and function in zebrafish neural circuits
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Towards fair principles for research software
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Msa transformer
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Accurate prediction of protein structures and interactions using a three-track neural network
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Highly accurate protein structure prediction with alphafold
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Adaptive machine learning for protein engineering
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Evidential deep learning for guided molecular property prediction and discovery
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Advancing mathematics by guiding human intuition with ai
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Genome-wide association studies
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Proximity extension assay in combination with next-generation sequencing for high-throughput proteome-wide analysis
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Proteome-wide mapping of short-lived proteins in human cells
Jiaming Li, Zhenying Cai, Laura Pontano Vaites, Ning Shen, Dylan C Mitchell, Edward L Huttlin, Joao A Paulo, Brian L Harry, and Steven P Gygi · 2021
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Learning transferable visual models from natural language supervision
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Webgpt: Browser-assisted question-answering with human feedback
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Zero-shot text-to-image generation
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Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, et al · 2021
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Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
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Optimizing risk-based breast cancer screening policies with reinforcement learning
Adam Yala, Peter G. Mikhael, Constance Lehman, Gigin Lin, Fredrik Strand, Yung-Liang Wan, Kevin Hughes, Siddharth Satuluru, Thomas Kim, Imon Banerjee, Judy Gichoya, Hari Trivedi, and Regina Barzilay · 2022
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Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn · 2023
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