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Creating end-to-end bioinformatics workflows requires diverse domain expertise, which poses challenges for both junior and senior researchers as it demands a deep understanding of both genomics concepts and computational techniques.
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W · 1940
Earlier work this paper cites.
The rast server: rapid annotations using subsystems technology
Aziz, R. K., Bartels, D., Best, A. A., DeJongh, M., Disz, T., Edwards, R. A., Formsma, K., Gerdes, S., Glass, E. M., Kubal, M., et al · 2008
Earlier work this paper cites.
Biostar: an online question & answer resource for the bioinformatics community
Parnell, L. D., Lindenbaum, P., Shameer, K., Dall’Olio, G. M., Swan, D. C., Jensen, L. J., Cockell, S. J., Pedersen, B. S., Mangan, M. E., Miller, C. A., et al · 2011
Earlier work this paper cites.
Star: ultrafast universal rna-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., and Gingeras, T. R · 2012
Earlier work this paper cites.
The software ontology (swo): a resource for reproducibility in biomedical data analysis, curation and digital preservation
Malone, J., Brown, A., Lister, A. L., Ison, J., Hull, D., Parkinson, H., and Stevens, R · 2014
Earlier work this paper cites.
Biocontainers: an open-source and community-driven framework for software standardization
da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., et al · 2017
Earlier work this paper cites.
Nextflow enables reproducible computational workflows
Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., and Notredame, C · 2017
Earlier work this paper cites.
A guide for designing and analyzing rna-seq data
Chatterjee, A., Ahn, A., Rodger, E. J., Stockwell, P. A., and Eccles, M. R · 2018
Earlier work this paper cites.
Recommendations for the packaging and containerizing of bioinformatics software
Gruening, B., Sallou, O., Moreno, P., da Veiga Leprevost, F., Ménager, H., Søndergaard, D., Röst, H., Sachsenberg, T., O’connor, B., Madeira, F., et al · 2019
Earlier work this paper cites.
Graph-based genome alignment and genotyping with hisat2 and hisat-genotype
Kim, D., Paggi, J. M., Park, C., Bennett, C., and Salzberg, S. L · 2019
Earlier work this paper cites.
The nf-core framework for community-curated bioinformatics pipelines
Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., and Nahnsen, S · 2020
Earlier work this paper cites.
edamontology/edamontology: Edam 1.25, June 2020
Ison, J., Kalaš, M., Ménager, H., Willighagen, E., Grüning, B., and Ignard, A · 2020
Earlier work this paper cites.
Edam: the bioscientific data analysis ontology (update 2021), 2022
Black, M., Lamothe, L., Eldakroury, H., et al · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
Earlier work this paper cites.
React: Synergizing reasoning and acting in language models
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y · 2022
Earlier work this paper cites.
Chatgpt, a powerful language model and its potential uses in bioinformatics
Bhardwaj, S., and Hasija, Y · 2023
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Autonomous chemical research with large language models
Boiko, D. A., MacKnight, R., Kline, B., and Gomes, G · 2023
Earlier work this paper cites.
Chemcrow: Augmenting large-language models with chemistry tools
Bran, A. M., Cox, S., Schilter, O., Baldassari, C., White, A. D., and Schwaller, P · 2023
Earlier work this paper cites.
Two failures of self-consistency in the multi-step reasoning of llms
Chen, A., Phang, J., Parrish, A., Padmakumar, V., Zhao, C., Bowman, S. R., and Cho, K · 2023
Earlier work this paper cites.
Bioinfo-bench: A simple benchmark framework for llm bioinformatics skills evaluation
Chen, Q., and Deng, C · 2023
Earlier work this paper cites.
What can large language models do in chemistry? a comprehensive benchmark on eight tasks
Guo, T., Nan, B., Liang, Z., Guo, Z., Chawla, N., Wiest, O., Zhang, X., et al · 2023
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Camel: Communicative agents for” mind” exploration of large language model society
Li, G., Hammoud, H., Itani, H., Khizbullin, D., and Ghanem, B · 2023
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Agentbench: Evaluating llms as agents
Liu, X., Yu, H., Zhang, H., Xu, Y., Lei, X., Lai, H., Gu, Y., Ding, H., Men, K., Yang, K., et al · 2023
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Ten quick tips for harnessing the power of chatgpt in computational biology
Lubiana, T., Lopes, R., Medeiros, P., Silva, J. C., Goncalves, A. N. A., Maracaja-Coutinho, V., and Nakaya, H. I · 2023
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Large language models generate functional protein sequences across diverse families
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., et al · 2023
Metacognitive capabilities of llms: An exploration in mathematical problem solving
Didolkar, A., Goyal, A., Ke, N. R., Guo, S., Valko, M., Lillicrap, T., Rezende, D., Bengio, Y., Mozer, M., and Arora, S · 2024
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Empowering biomedical discovery with ai agents
Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., and Zitnik, M · 2024
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When can llms actually correct their own mistakes? a critical survey of self-correction of llms
Kamoi, R., Zhang, Y., Zhang, N., Han, J., and Zhang, R · 2024
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Integrating large language models in bioinformatics education for medical students: Opportunities and challenges
Kang, K., Yang, Y., Wu, Y., and Luo, R · 2024
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Internal consistency and self-feedback in large language models: A survey
Liang, X., Song, S., Zheng, Z., Wang, H., Yu, Q., Li, X., Li, R.-H., Wang, Y., Wang, Z., Xiong, F., et al · 2024
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Cited alongside, same era.
Pan, L., Saxon, M., Xu, W., Nathani, D., Wang, X., and Wang, W. Y · 2023
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A beginner’s guide to bioinformatics
Patel, K · 2023
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Many bioinformatics programming tasks can be automated with chatgpt
Piccolo, S. R., Denny, P., Luxton-Reilly, A., Payne, S., and Ridge, P. G · 2023
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Self-evaluation improves selective generation in large language models
Ren, J., Zhao, Y., Vu, T., Liu, P. J., and Lakshminarayanan, B · 2023
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Biollmbench: A comprehensive benchmarking of large language models in bioinformatics
Sarwal, V., Munteanu, V., Suhodolschi, T., Ciorba, D., Eskin, E., Wang, W., and Mangul, S · 2023
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Enhancing trust in llm-based ai automation agents: New considerations and future challenges
Schwartz, S., Yaeli, A., and Shlomov, S · 2023
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Empowering beginners in bioinformatics with chatgpt
Shue, E., Liu, L., Li, B., Feng, Z., Li, X., and Hu, G · 2023
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Augmenting large language models with chemistry tools
M. Bran, A., Cox, S., Schilter, O., Baldassari, C., White, A. D., and Schwaller, P · 2024
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Evaluating large language models as agents in the clinic
Mehandru, N., Miao, B. Y., Almaraz, E. R., Sushil, M., Butte, A. J., and Alaa, A · 2024
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Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2024
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Revolutionizing mental health care through langchain: A journey with a large language model
Singh, A., Ehtesham, A., Mahmud, S., and Kim, J.-H · 2024
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Simulating human strategic behavior: Comparing single and multi-agent llms
Sreedhar, K., and Chilton, L · 2024
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Biocoder: a benchmark for bioinformatics code generation with large language models
Tang, X., Qian, B., Gao, R., Chen, J., Chen, X., and Gerstein, M. B · 2024
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A survey on self-evolution of large language models
Tao, Z., Lin, T.-E., Chen, X., Li, H., Wu, Y., Li, Y., Jin, Z., Huang, F., Tao, D., and Zhou, J · 2024
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A survey on large language model based autonomous agents
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al · 2024
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Where do large language models fail when generating code?
Wang, Z., Zhou, Z., Song, D., Huang, Y., Chen, S., Ma, L., and Zhang, T · 2024
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Cellagent: An llm-driven multi-agent framework for automated single-cell data analysis
Xiao, Y., Liu, J., Zheng, Y., Xie, X., Hao, J., Li, M., Wang, R., Ni, F., Li, Y., Luo, J., et al · 2024
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Bioinformatics agent (bia): Unleashing the power of large language models to reshape bioinformatics workflow
Xin, Q., Kong, Q., Ji, H., Shen, Y., Liu, Y., Sun, Y., Zhang, Z., Li, Z., Xia, X., Deng, B., et al · 2024
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An evaluation of large language models in bioinformatics research
Yin, H., Gu, Z., Wang, F., Abuduhaibaier, Y., Zhu, Y., Tu, X., Hua, X.-S., Luo, X., and Sun, Y · 2024
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Agent-as-a-judge: Evaluate agents with agents
Zhuge, M., Zhao, C., Ashley, D., Wang, W., Khizbullin, D., Xiong, Y., Liu, Z., Chang, E., Krishnamoorthi, R., Tian, Y., et al · 2024
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Prokka: rapid prokaryotic genome annotation
Seemann, T · 2069
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