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Flynn CD, Chang D. Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities. Diagnostics (Basel) 2024; 14:1100. [PMID: 38893627 PMCID: PMC11172335 DOI: 10.3390/diagnostics14111100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.
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Affiliation(s)
- Connor D. Flynn
- Department of Chemistry, Weinberg College of Arts & Sciences, Northwestern University, Evanston, IL 60208, USA
| | - Dingran Chang
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60208, USA
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Parker BL, Calkin AC, Seldin MM, Keating MF, Tarling EJ, Yang P, Moody SC, Liu Y, Zerenturk EJ, Needham EJ, Miller ML, Clifford BL, Morand P, Watt MJ, Meex RCR, Peng KY, Lee R, Jayawardana K, Pan C, Mellett NA, Weir JM, Lazarus R, Lusis AJ, Meikle PJ, James DE, de Aguiar Vallim TQ, Drew BG. An integrative systems genetic analysis of mammalian lipid metabolism. Nature 2019; 567:187-193. [PMID: 30814737 PMCID: PMC6656374 DOI: 10.1038/s41586-019-0984-y] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 01/23/2019] [Indexed: 12/16/2022]
Abstract
Dysregulation of lipid homeostasis is a precipitating event in the pathogenesis and progression of hepatosteatosis and metabolic syndrome. These conditions are highly prevalent in developed societies and currently have limited options for diagnostic and therapeutic intervention. Here, using a proteomic and lipidomic-wide systems genetic approach, we interrogated lipid regulatory networks in 107 genetically distinct mouse strains to reveal key insights into the control and network structure of mammalian lipid metabolism. These include the identification of plasma lipid signatures that predict pathological lipid abundance in the liver of mice and humans, defining subcellular localization and functionality of lipid-related proteins, and revealing functional protein and genetic variants that are predicted to modulate lipid abundance. Trans-omic analyses using these datasets facilitated the identification and validation of PSMD9 as a previously unknown lipid regulatory protein. Collectively, our study serves as a rich resource for probing mammalian lipid metabolism and provides opportunities for the discovery of therapeutic agents and biomarkers in the setting of hepatic lipotoxicity.
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Affiliation(s)
- Benjamin L Parker
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Anna C Calkin
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia.
- Central Clinical School, Department of Medicine, Monash University, Melbourne, Victoria, Australia.
| | - Marcus M Seldin
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Michael F Keating
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Central Clinical School, Department of Medicine, Monash University, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Elizabeth J Tarling
- Department of Medicine, Division of Cardiology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
- Molecular Biology Institute, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Pengyi Yang
- Charles Perkins Centre, School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
| | - Sarah C Moody
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Yingying Liu
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Eser J Zerenturk
- Lipid Metabolism & Cardiometabolic Disease Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Elise J Needham
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Matthew L Miller
- Molecular Biology Institute, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Bethan L Clifford
- Department of Medicine, Division of Cardiology, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Pauline Morand
- Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Matthew J Watt
- Department of Physiology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Ruth C R Meex
- Department of Physiology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Kang-Yu Peng
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | | | - Kaushala Jayawardana
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Calvin Pan
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Natalie A Mellett
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Jacquelyn M Weir
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Ross Lazarus
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - Aldons J Lusis
- Department of Human Genetics/Medicine, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Peter J Meikle
- Metabolomics Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
| | - David E James
- Metabolic Systems Biology Laboratory, Charles Perkins Centre, School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Thomas Q de Aguiar Vallim
- Department of Medicine, Division of Cardiology, University of California Los Angeles (UCLA), Los Angeles, CA, USA.
- Molecular Biology Institute, University of California Los Angeles (UCLA), Los Angeles, CA, USA.
- Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA.
| | - Brian G Drew
- Central Clinical School, Department of Medicine, Monash University, Melbourne, Victoria, Australia.
- Molecular Metabolism & Ageing Laboratory, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia.
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Kopriva I, Filipović M. A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels. BMC Bioinformatics 2011; 12:496. [PMID: 22208882 PMCID: PMC3292585 DOI: 10.1186/1471-2105-12-496] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2011] [Accepted: 12/30/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Bioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue. RESULTS The method proposed here is applied to well-studied protein and genomic datasets of ovarian, prostate and colon cancers to extract components for disease prediction. It achieves average sensitivities of: 96.2 (sd = 2.7%), 97.6% (sd = 2.8%) and 90.8% (sd = 5.5%) and average specificities of: 93.6% (sd = 4.1%), 99% (sd = 2.2%) and 79.4% (sd = 9.8%) in 100 independent two-fold cross-validations. CONCLUSIONS We propose an additive mixture model of a sample for feature extraction using, in principle, sparseness constrained factorization on a sample-by-sample basis. As opposed to that, existing methods factorize complete dataset simultaneously. The sample model is composed of a reference sample representing control and/or case (disease) groups and a test sample. Each sample is decomposed into two or more components that are selected automatically (without using label information) as control specific, case specific and not differentially expressed (neutral). The number of components is determined by cross-validation. Automatic assignment of features (m/z ratios or genes) to particular component is based on thresholds estimated from each sample directly. Due to the locality of decomposition, the strength of the expression of each feature across the samples can vary. Yet, they will still be allocated to the related disease and/or control specific component. Since label information is not used in the selection process, case and control specific components can be used for classification. That is not the case with standard factorization methods. Moreover, the component selected by proposed method as disease specific can be interpreted as a sub-mode and retained for further analysis to identify potential biomarkers. As opposed to standard matrix factorization methods this can be achieved on a sample (experiment)-by-sample basis. Postulating one or more components with indifferent features enables their removal from disease and control specific components on a sample-by-sample basis. This yields selected components with reduced complexity and generally, it increases prediction accuracy.
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Affiliation(s)
- Ivica Kopriva
- Division of Laser and Atomic R&D, Ruđer Bošković Institute, Bijenička cesta 54, 10000 Zagreb, Croatia.
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