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Bongirwar R, Shukla P. Engineering regulatory networks of cyanobacteria. Trends Biotechnol 2024; 42:949-952. [PMID: 38296717 DOI: 10.1016/j.tibtech.2023.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 02/02/2024]
Abstract
Engineering a cell's regulatory networks to dynamically control gene expression has been considered a new frontier in biological engineering. In cyanobacteria, the lack of well-characterized, modular gene regulatory elements makes regulatory network engineering challenging. Here, we suggest potential tools to modify various gene expression steps in cyanobacterial regulatory networks.
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Affiliation(s)
- Riya Bongirwar
- Enzyme Technology and Protein Bioinformatics Laboratory, School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India
| | - Pratyoosh Shukla
- Enzyme Technology and Protein Bioinformatics Laboratory, School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi 221005, Uttar Pradesh, India.
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Yang G, Li J, Hu J, Shi JY. Recognition of cyanobacteria promoters via Siamese network-based contrastive learning under novel non-promoter generation. Brief Bioinform 2024; 25:bbae193. [PMID: 38701419 PMCID: PMC11066903 DOI: 10.1093/bib/bbae193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/08/2024] [Accepted: 04/05/2024] [Indexed: 05/05/2024] Open
Abstract
It is a vital step to recognize cyanobacteria promoters on a genome-wide scale. Computational methods are promising to assist in difficult biological identification. When building recognition models, these methods rely on non-promoter generation to cope with the lack of real non-promoters. Nevertheless, the factitious significant difference between promoters and non-promoters causes over-optimistic prediction. Moreover, designed for E. coli or B. subtilis, existing methods cannot uncover novel, distinct motifs among cyanobacterial promoters. To address these issues, this work first proposes a novel non-promoter generation strategy called phantom sampling, which can eliminate the factitious difference between promoters and generated non-promoters. Furthermore, it elaborates a novel promoter prediction model based on the Siamese network (SiamProm), which can amplify the hidden difference between promoters and non-promoters through a joint characterization of global associations, upstream and downstream contexts, and neighboring associations w.r.t. k-mer tokens. The comparison with state-of-the-art methods demonstrates the superiority of our phantom sampling and SiamProm. Both comprehensive ablation studies and feature space illustrations also validate the effectiveness of the Siamese network and its components. More importantly, SiamProm, upon our phantom sampling, finds a novel cyanobacterial promoter motif ('GCGATCGC'), which is palindrome-patterned, content-conserved, but position-shifted.
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Affiliation(s)
- Guang Yang
- School of Life Sciences, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China
| | - Jianing Li
- School of Computer Science, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China
| | - Jinlu Hu
- School of Life Sciences, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China
| | - Jian-Yu Shi
- School of Life Sciences, Northwestern Polytechnical University, Xi’an, Shaanxi, 710072, China
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Tan TF, Thirunavukarasu AJ, Campbell JP, Keane PA, Pasquale LR, Abramoff MD, Kalpathy-Cramer J, Lum F, Kim JE, Baxter SL, Ting DSW. Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges. OPHTHALMOLOGY SCIENCE 2023; 3:100394. [PMID: 37885755 PMCID: PMC10598525 DOI: 10.1016/j.xops.2023.100394] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/07/2023] [Accepted: 08/30/2023] [Indexed: 10/28/2023]
Abstract
The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: "large language models," "generative artificial intelligence," "ophthalmology," "ChatGPT," and "eye," based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders' perspectives-including patients, physicians, and policymakers-the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Ting Fang Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Arun James Thirunavukarasu
- University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Corpus Christi College, University of Cambridge, Cambridge, United Kingdom
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Pearse A. Keane
- Moorfields Eye Hospital, University of College London, London, United Kingdom
| | - Louis R. Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - Michael D. Abramoff
- American Medical Association's Digital Medicine Payment Advisory Group (DMPAG) Artificial Intelligence Workgroup, American Medical Association, Chicago, Illinois
- Department of Ophthalmology, University of Iowa, Iowa City, Iowa
- Digital Diagnostics, Inc, Coralville, Iowa
| | | | - Flora Lum
- American Academy of Ophthalmology, San Francisco, California
| | - Judy E. Kim
- Department of Ophthalmology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Sally L. Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, La Jolla, California
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Byers Eye Institute, Stanford University, Stanford, California
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Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med 2023; 29:1930-1940. [PMID: 37460753 DOI: 10.1038/s41591-023-02448-8] [Citation(s) in RCA: 335] [Impact Index Per Article: 335.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 06/08/2023] [Indexed: 08/17/2023]
Abstract
Large language models (LLMs) can respond to free-text queries without being specifically trained in the task in question, causing excitement and concern about their use in healthcare settings. ChatGPT is a generative artificial intelligence (AI) chatbot produced through sophisticated fine-tuning of an LLM, and other tools are emerging through similar developmental processes. Here we outline how LLM applications such as ChatGPT are developed, and we discuss how they are being leveraged in clinical settings. We consider the strengths and limitations of LLMs and their potential to improve the efficiency and effectiveness of clinical, educational and research work in medicine. LLM chatbots have already been deployed in a range of biomedical contexts, with impressive but mixed results. This review acts as a primer for interested clinicians, who will determine if and how LLM technology is used in healthcare for the benefit of patients and practitioners.
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Affiliation(s)
- Arun James Thirunavukarasu
- University of Cambridge School of Clinical Medicine, Cambridge, UK
- Corpus Christi College, University of Cambridge, Cambridge, UK
| | - Darren Shu Jeng Ting
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- Birmingham and Midland Eye Centre, Birmingham, UK
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, UK
| | - Kabilan Elangovan
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Laura Gutierrez
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ting Fang Tan
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Daniel Shu Wei Ting
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore Medical School, Singapore, Singapore.
- Byers Eye Institute, Stanford University, Palo Alto, CA, USA.
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Choi SR, Lee M. Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review. BIOLOGY 2023; 12:1033. [PMID: 37508462 PMCID: PMC10376273 DOI: 10.3390/biology12071033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/18/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023]
Abstract
The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.
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Affiliation(s)
- Sanghyuk Roy Choi
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
| | - Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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