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Li Pomi F, Papa V, Borgia F, Vaccaro M, Pioggia G, Gangemi S. Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases. Life (Basel) 2024; 14:516. [PMID: 38672786 PMCID: PMC11051135 DOI: 10.3390/life14040516] [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: 03/29/2024] [Revised: 04/10/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.
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Affiliation(s)
- Federica Li Pomi
- Department of Precision Medicine in Medical, Surgical and Critical Care (Me.Pre.C.C.), University of Palermo, 90127 Palermo, Italy;
| | - Vincenzo Papa
- Department of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, Italy; (V.P.); (S.G.)
| | - Francesco Borgia
- Department of Clinical and Experimental Medicine, Section of Dermatology, University of Messina, 98125 Messina, Italy;
| | - Mario Vaccaro
- Department of Clinical and Experimental Medicine, Section of Dermatology, University of Messina, 98125 Messina, Italy;
| | - Giovanni Pioggia
- Institute for Biomedical Research and Innovation (IRIB), National Research Council of Italy (CNR), 98164 Messina, Italy;
| | - Sebastiano Gangemi
- Department of Clinical and Experimental Medicine, School and Operative Unit of Allergy and Clinical Immunology, University of Messina, 98125 Messina, Italy; (V.P.); (S.G.)
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2
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Ghandikota SK, Jegga AG. Application of artificial intelligence and machine learning in drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:171-211. [PMID: 38789178 DOI: 10.1016/bs.pmbts.2024.03.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.
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Affiliation(s)
- Sudhir K Ghandikota
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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3
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Liu Z, Wang X, Ma Y, Lin Y, Wang G. Artificial intelligence in psoriasis: Where we are and where we are going. Exp Dermatol 2023; 32:1884-1899. [PMID: 37740587 DOI: 10.1111/exd.14938] [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: 06/15/2023] [Revised: 09/05/2023] [Accepted: 09/09/2023] [Indexed: 09/24/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual-based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e-health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.
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Affiliation(s)
- Zhenhua Liu
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
- Department of Dermatology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Xinyu Wang
- Department of Economics, Finance and Healthcare Administration, Valdosta State University, Valdosta, Georgia, USA
| | - Yao Ma
- Student Brigade of Basic Medicine School, Fourth Military Medical University, Xi'an, China
| | - Yiting Lin
- Department of Dermatology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
| | - Gang Wang
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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4
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van Breugel M, Fehrmann RSN, Bügel M, Rezwan FI, Holloway JW, Nawijn MC, Fontanella S, Custovic A, Koppelman GH. Current state and prospects of artificial intelligence in allergy. Allergy 2023; 78:2623-2643. [PMID: 37584170 DOI: 10.1111/all.15849] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/08/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023]
Abstract
The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.
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Affiliation(s)
- Merlijn van Breugel
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- MIcompany, Amsterdam, the Netherlands
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Faisal I Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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Lunge SB, Shetty NS, Sardesai VR, Karagaiah P, Yamauchi PS, Weinberg JM, Kircik L, Giulini M, Goldust M. Therapeutic application of machine learning in psoriasis: A Prisma systematic review. J Cosmet Dermatol 2023; 22:378-382. [PMID: 35621249 DOI: 10.1111/jocd.15122] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 05/15/2022] [Accepted: 05/24/2022] [Indexed: 11/27/2022]
Abstract
Dermatology, being a predominantly visual-based diagnostic field, has found itself to be at the epitome of artificial intelligence (AI)-based advances. Machine learning (ML), a subset of AI, goes a step further by recognizing patterns from data and teaches machines to automatically learn tasks. Although artificial intelligence in dermatology is mostly developed in melanoma and skin cancer diagnosis, advances in AI and ML have gone far ahead and found its application in ulcer assessment, psoriasis, atopic dermatitis, onychomycosis, etc. This article is focused on the application of ML in the therapeutic aspect of psoriasis.
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Affiliation(s)
- Snehal Balvant Lunge
- Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth (DTU) Medical College and Hospital, Pune, India
| | - Nandini Sundar Shetty
- Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth (DTU) Medical College and Hospital, Pune, India
| | - Vidyadhar R Sardesai
- Department of Dermatology, Venereology and Leprosy, Bharati Vidyapeeth (DTU) Medical College and Hospital, Pune, India
| | - Priyanka Karagaiah
- Department of dermatology, Bangalore Medical College and Research Institute, Bangalore, India
| | - Paul S Yamauchi
- Dermatology Institute and Skin Care Center, Santa Monica, California, USA
- Division of Dermatology, David Geffen School of Medicine at University of California, Los Angeles, California, USA
| | | | - Leon Kircik
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mario Giulini
- Department of Dermatology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Mohamad Goldust
- Department of Dermatology, University Medical Center Mainz, Mainz, Germany
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6
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Ahmed F, Gi Ho S, Samantasinghar A, Memon FH, Rahim CSA, Soomro AM, Pratibha, Sunildutt N, Kim KH, Choi KH. Drug repurposing in psoriasis, performed by reversal of disease-associated gene expression profiles. Comput Struct Biotechnol J 2022; 20:6097-6107. [DOI: 10.1016/j.csbj.2022.10.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/09/2022] [Accepted: 10/30/2022] [Indexed: 11/10/2022] Open
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7
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Hossain MZ, Daskalaki E, Brüstle A, Desborough J, Lueck CJ, Suominen H. The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review. BMC Med Inform Decis Mak 2022; 22:242. [PMID: 36109726 PMCID: PMC9476596 DOI: 10.1186/s12911-022-01985-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 09/02/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Multiple sclerosis (MS) is a neurological condition whose symptoms, severity, and progression over time vary enormously among individuals. Ideally, each person living with MS should be provided with an accurate prognosis at the time of diagnosis, precision in initial and subsequent treatment decisions, and improved timeliness in detecting the need to reassess treatment regimens. To manage these three components, discovering an accurate, objective measure of overall disease severity is essential. Machine learning (ML) algorithms can contribute to finding such a clinically useful biomarker of MS through their ability to search and analyze datasets about potential biomarkers at scale. Our aim was to conduct a systematic review to determine how, and in what way, ML has been applied to the study of MS biomarkers on data from sources other than magnetic resonance imaging.
Methods
Systematic searches through eight databases were conducted for literature published in 2014–2020 on MS and specified ML algorithms.
Results
Of the 1, 052 returned papers, 66 met the inclusion criteria. All included papers addressed developing classifiers for MS identification or measuring its progression, typically, using hold-out evaluation on subsets of fewer than 200 participants with MS. These classifiers focused on biomarkers of MS, ranging from those derived from omics and phenotypical data (34.5% clinical, 33.3% biological, 23.0% physiological, and 9.2% drug response). Algorithmic choices were dependent on both the amount of data available for supervised ML (91.5%; 49.2% classification and 42.3% regression) and the requirement to be able to justify the resulting decision-making principles in healthcare settings. Therefore, algorithms based on decision trees and support vector machines were commonly used, and the maximum average performance of 89.9% AUC was found in random forests comparing with other ML algorithms.
Conclusions
ML is applicable to determining how candidate biomarkers perform in the assessment of disease severity. However, applying ML research to develop decision aids to help clinicians optimize treatment strategies and analyze treatment responses in individual patients calls for creating appropriate data resources and shared experimental protocols. They should target proceeding from segregated classification of signals or natural language to both holistic analyses across data modalities and clinically-meaningful differentiation of disease.
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8
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Karami H, Nomiri S, Ghasemigol M, Mehrvarzian N, Derakhshani A, Fereidouni M, Mirimoghaddam M, Safarpour H. CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity. IET Syst Biol 2022; 16:173-185. [PMID: 35983595 PMCID: PMC9469792 DOI: 10.1049/syb2.12048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 06/11/2022] [Accepted: 07/20/2022] [Indexed: 11/20/2022] Open
Abstract
Alopecia Areata (AA) is characterised by an autoimmune response to hair follicles (HFs) and its exact pathobiology remains unclear. The current study aims to look into the molecular changes in the skin of AA patients as well as the potential underlying molecular mechanisms of AA in order to identify potential candidates for early detection and treatment of AA. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub genes, and mRNA-miRNA regulatory networks associated with AA. Furthermore, Chi2 as a machine-learning algorithm was used to compute the gene importance in AA. Finally, drug-target construction revealed the potential of repositioning drugs for the treatment of AA. Our analysis using four AA data sets established a network strongly correlated to AA pathogenicity based on GZMA, OXCT2, HOXC13, KRT40, COMP, CHAC1, and KRT83 hub genes. Interestingly, machine learning introduced these genes as important in AA pathogenicity. Besides that, using another ten data sets, we showed that CHAC1 could clearly distinguish AA from similar clinical phenotypes, such as scarring alopecia due to psoriasis. Also, two FDA-approved drug candidates and 30 experimentally validated miRNAs were identified that affected the co-expression network. Using transcriptome analysis, suggested CHAC1 as a potential diagnostic predictor to diagnose AA.
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Affiliation(s)
- Hassan Karami
- Student Research CommitteeFaculty of MedicineBirjand University of Medical SciencesBirjandIran
| | - Samira Nomiri
- Department of BiochemistryFaculty of MedicineBirjand University of Medical SciencesBirjandIran
| | | | - Niloufar Mehrvarzian
- Department of Pharmaceutical NanotechnologyFaculty of PharmacyMashhad University of Medical SciencesMashhadIran
| | - Afshin Derakhshani
- McCaig Institute, Hotchkiss Brain InstituteSnyder Institute for Chronic DiseasesUniversity of CalgaryCalgary, AlbertaCanada,Department of Biochemistry and Molecular BiologyUniversity of CalgaryCalgaryAlbertaCanada
| | - Mohammad Fereidouni
- Cellular and Molecular Research CenterBirjand University of Medical SciencesBirjandIran
| | | | - Hossein Safarpour
- Cellular and Molecular Research CenterBirjand University of Medical SciencesBirjandIran
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De Cock D, Myasoedova E, Aletaha D, Studenic P. Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs). Ther Adv Musculoskelet Dis 2022; 14:1759720X221105978. [PMID: 35794905 PMCID: PMC9251966 DOI: 10.1177/1759720x221105978] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/22/2022] [Indexed: 11/17/2022] Open
Abstract
Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.
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Affiliation(s)
- Diederik De Cock
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Elena Myasoedova
- Division of Rheumatology, Department of Internal Medicine and Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Daniel Aletaha
- Division of Rheumatology, Department of Internal Medicine 3, Medical University Vienna, Vienna, Austria
| | - Paul Studenic
- Division of Rheumatology, Department of Internal Medicine 3, Medical University Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
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Staszak M, Staszak K, Wieszczycka K, Bajek A, Roszkowski K, Tylkowski B. Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1568] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Maciej Staszak
- Institute of Technology and Chemical Engineering Poznan University of Technology Poznan Poland
| | - Katarzyna Staszak
- Institute of Technology and Chemical Engineering Poznan University of Technology Poznan Poland
| | - Karolina Wieszczycka
- Institute of Technology and Chemical Engineering Poznan University of Technology Poznan Poland
| | - Anna Bajek
- Department of Tissue Engineering Collegium Medicum, Nicolaus Copernicus University Bydgoszcz Poland
| | - Krzysztof Roszkowski
- Department of Oncology Collegium Medicum Nicolaus Copernicus University Bydgoszcz Poland
| | - Bartosz Tylkowski
- Department of Chemical Engineering University Rovira i Virgili Tarragona Spain
- Eurecat, Centre Tecnològic de Catalunya Chemical Technologies Unit Tarragona Spain
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11
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Allahgholi M, Rahmani H, Javdani D, Sadeghi-Adl Z, Bender A, Módos D, Weiss G. DDREL: From drug-drug relationships to drug repurposing. INTELL DATA ANAL 2022. [DOI: 10.3233/ida-215745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.
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Affiliation(s)
- Milad Allahgholi
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Hossein Rahmani
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Delaram Javdani
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Zahra Sadeghi-Adl
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Dezsö Módos
- Quadram Institute Bioscience, Norwich Research Park, Norwich, Norfolk, UK
- Earlham Institute, Norwich Research Park, Norwich, Norfolk, UK
| | - Gerhard Weiss
- Department of Data Science and Knowledge Engineering (DKE), Maastricht University, Maastricht, The Netherlands
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. [Translated article] Artificial intelligence in dermatology: A threat or an opportunity? ACTAS DERMO-SIFILIOGRAFICAS 2022. [DOI: 10.1016/j.ad.2021.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022] Open
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13
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Inteligencia artificial en dermatología: ¿amenaza u oportunidad? ACTAS DERMO-SIFILIOGRAFICAS 2022; 113:30-46. [DOI: 10.1016/j.ad.2021.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 07/18/2021] [Indexed: 11/25/2022] Open
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14
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Pandya S, Thakur A, Saxena S, Jassal N, Patel C, Modi K, Shah P, Joshi R, Gonge S, Kadam K, Kadam P. A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions. SENSORS (BASEL, SWITZERLAND) 2021; 21:7786. [PMID: 34883787 PMCID: PMC8659723 DOI: 10.3390/s21237786] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/17/2021] [Accepted: 11/21/2021] [Indexed: 12/13/2022]
Abstract
The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.
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Affiliation(s)
- Sharnil Pandya
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Aanchal Thakur
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Santosh Saxena
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Nandita Jassal
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Chirag Patel
- Computer Science & Engineering, Devang Patel Institute of Advance Technology and Research, Changa 388421, India;
| | - Kirit Modi
- Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar 384315, India;
| | - Pooja Shah
- Information Technology Department, Gandhinagar Institute of Technology, Ahmedabad 382010, India;
| | - Rahul Joshi
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Sudhanshu Gonge
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Kalyani Kadam
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
| | - Prachi Kadam
- Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; (A.T.); (S.S.); (N.J.); (R.J.); (S.G.); (K.K.); (P.K.)
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15
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Kingsmore KM, Puglisi CE, Grammer AC, Lipsky PE. An introduction to machine learning and analysis of its use in rheumatic diseases. Nat Rev Rheumatol 2021; 17:710-730. [PMID: 34728818 DOI: 10.1038/s41584-021-00708-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/04/2021] [Indexed: 02/07/2023]
Abstract
Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.
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Affiliation(s)
| | | | - Amrie C Grammer
- AMPEL BioSolutions and RILITE Research Institute, Charlottesville, VA, USA
| | - Peter E Lipsky
- AMPEL BioSolutions and RILITE Research Institute, Charlottesville, VA, USA
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16
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Suresh Kumar R, Dhanagopal R, Sasi Kumar S. Enriched Optimization Algorithm for Effective Skin Disease Prediction Using Soft Computing Techniques. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS 2021. [DOI: 10.1166/jmihi.2021.3882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
In recent years, a usual worldwide problem is a skin disease—the diagnostics of infection and skin disease prediction based on the data mining techniques. The precise and cost-effective treatments obtain a technologybased data mining system that can consider making the right decision.
Depends on data, there are 34 UCI datasets have in the skin disease prediction. All of the datasets are not much important when predicting the skin disease problem. In this study, the essential datasets to be analyzed because they only give the best accuracy in skin disease prediction. For
an outstanding selection of allocation, to propose a novel feature selection approaches, Enriched Fruitfly Optimization Algorithm (EFOA), and Ensemble Classifiers that are helps for an early stage of skin disease prediction. A hybrid technique through the three essential hybrid feature selection
approaches such as Chi-Square method, Information Gain method, and Principal Component Analysis (PCA) methods that are combined for better feature selection results. Based on the skin disease dataset, the resultant feature selection approach generated the reduced data subset. Then, the Enriched
Fruitfly Optimization Algorithm (EFOA) offers the optimization of reduced data subset. Here, the accuracy estimation is the vital factor to optimize the effective and best prediction of skin disease affected regions. Afterward, the classification performs to classify the EFOA based optimized
result by using the six different classification methods. Where, the classification helps to analyze the optimized results, which offers the better classification procedure. To predict the base learner’s performance, to utilize the Naive Bayesian, K-Nearest Neighbour, Decision Tree,
Support Vector Machine, Random Forest, and Multilayer Perceptron (MLP) to classify the optimized result. Then, the ensemble techniques used to analyze the classifier’s results through the 3 different methods like Bagging, Boosting, Stacking, added on the base learners to improve the
proposed work. Based on the performance, the base learners’ performance is larger than the input dataset. The base learner’s parameters are essential to calculate the accuracy of skin disease prediction performance. The performance of the proposed method will take and compare to
each base learner, and the performance shows the accurate skin disease prediction improvement with other existing methods.
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Affiliation(s)
- R. Suresh Kumar
- Center for System Design, Department of Electronics and Communication Engineering, Chennai Institute of Technology, Kundrathur, Chennai 600069, Tamilnadu, India
| | - R. Dhanagopal
- Center for System Design, Department of Electronics and Communication Engineering, Chennai Institute of Technology, Kundrathur, Chennai 600069, Tamilnadu, India
| | - S. Sasi Kumar
- Department of Electronics and Communication Engineering, Hindustan Institute of Technology and Science, Padur, Chennai, 603103, Tamilnadu, India
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17
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Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Artificial intelligence in dermatology: A threat or an opportunity? ACTAS DERMO-SIFILIOGRAFICAS 2021. [DOI: 10.1016/j.adengl.2021.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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18
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Mohanty S, Rashid MHA, Mohanty C, Swayamsiddha S. Modern computational intelligence based drug repurposing for diabetes epidemic. Diabetes Metab Syndr 2021; 15:102180. [PMID: 34186343 DOI: 10.1016/j.dsx.2021.06.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 06/12/2021] [Accepted: 06/14/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND AND AIM Objectives are to explore recent advances in discovery of new antidiabetic agents using repurposing strategies and to discuss modern technologies used for drug repurposing highlighting diabetic specific web portal. METHODS Recent literature were studied and analyzed from various sources such as Scopus, PubMed, and IEEE Xplore databases. RESULTS Drugs like Niclosamideethanolamine, Methazolamide, Diacerein, Berberine, Clobetasol, etc. with possibility of repurposing to curb diabetes can be potential late-stage clinical candidates, providing access to information on pharmacology, formulation, and probable toxicity if any. CONCLUSIONS With collaboration of artificial intelligence (AI) with pharmacology, the efficiency of drug repurposing can improve significantly.
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Affiliation(s)
- Sweta Mohanty
- School of Applied Science, KIIT University, Bhubaneswar, Odisha, India
| | | | - Chandana Mohanty
- School of Applied Science, KIIT University, Bhubaneswar, Odisha, India.
| | - Swati Swayamsiddha
- School of Electronics Engineering, KIIT University, Bhubaneswar, Odisha, India.
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19
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Patrick MT, Bardhi R, Raja K, He K, Tsoi LC. Advancement in predicting interactions between drugs used to treat psoriasis and its comorbidities by integrating molecular and clinical resources. J Am Med Inform Assoc 2021; 28:1159-1167. [PMID: 33544847 DOI: 10.1093/jamia/ocaa335] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 12/14/2020] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE Drug-drug interactions (DDIs) can result in adverse and potentially life-threatening health consequences; however, it is challenging to predict potential DDIs in advance. We introduce a new computational approach to comprehensively assess the drug pairs which may be involved in specific DDI types by combining information from large-scale gene expression (984 transcriptomic datasets), molecular structure (2159 drugs), and medical claims (150 million patients). MATERIALS AND METHODS Features were integrated using ensemble machine learning techniques, and we evaluated the DDIs predicted with a large hospital-based medical records dataset. Our pipeline integrates information from >30 different resources, including >10 000 drugs and >1.7 million drug-gene pairs. We applied our technique to predict interactions between 37 611 drug pairs used to treat psoriasis and its comorbidities. RESULTS Our approach achieves >0.9 area under the receiver operator curve (AUROC) for differentiating 11 861 known DDIs from 25 750 non-DDI drug pairs. Significantly, we demonstrate that the novel DDIs we predict can be confirmed through independent data sources and supported using clinical medical records. CONCLUSIONS By applying machine learning and taking advantage of molecular, genomic, and health record data, we are able to accurately predict potential new DDIs that can have an impact on public health.
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Affiliation(s)
- Matthew T Patrick
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Redina Bardhi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA.,School of Medicine, Wayne State University, Detroit, Michigan, USA
| | - Kalpana Raja
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA.,Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Kevin He
- Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Lam C Tsoi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, Michigan, USA.,Department of Biostatistics, Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
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20
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Pezoulas VC, Papaloukas C, Veyssiere M, Goules A, Tzioufas AG, Soumelis V, Fotiadis DI. A computational workflow for the detection of candidate diagnostic biomarkers of Kawasaki disease using time-series gene expression data. Comput Struct Biotechnol J 2021; 19:3058-3068. [PMID: 34136104 PMCID: PMC8178098 DOI: 10.1016/j.csbj.2021.05.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/17/2021] [Accepted: 05/20/2021] [Indexed: 12/15/2022] Open
Abstract
Unlike autoimmune diseases, there is no known constitutive and disease-defining biomarker for systemic autoinflammatory diseases (SAIDs). Kawasaki disease (KD) is one of the "undiagnosed" types of SAIDs whose pathogenic mechanism and gene mutation still remain unknown. To address this issue, we have developed a sequential computational workflow which clusters KD patients with similar gene expression profiles across the three different KD phases (Acute, Subacute and Convalescent) and utilizes the resulting clustermap to detect prominent genes that can be used as diagnostic biomarkers for KD. Self-Organizing Maps (SOMs) were employed to cluster patients with similar gene expressions across the three phases through inter-phase and intra-phase clustering. Then, false discovery rate (FDR)-based feature selection was applied to detect genes that significantly deviate across the per-phase clusters. Our results revealed five genes as candidate biomarkers for KD diagnosis, namely, the HLA-DQB1, HLA-DRA, ZBTB48, TNFRSF13C, and CASD1. To our knowledge, these five genes are reported for the first time in the literature. The impact of the discovered genes for KD diagnosis against the known ones was demonstrated by training boosting ensembles (AdaBoost and XGBoost) for KD classification on common platform and cross-platform datasets. The classifiers which were trained on the proposed genes from the common platform data yielded an average increase by 4.40% in accuracy, 5.52% in sensitivity, and 3.57% in specificity than the known genes in the Acute and Subacute phases, followed by a notable increase by 2.30% in accuracy, 2.20% in sensitivity, and 4.70% in specificity in the cross-platform analysis.
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Affiliation(s)
- Vasileios C. Pezoulas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina GR45110, Greece
| | - Costas Papaloukas
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina GR45110, Greece
- Department of Biological Applications and Technology, University of Ioannina, Ioannina GR45100, Greece
| | - Maëva Veyssiere
- INSERM U976, Human Immunology, Physiopathology and Immunotherapy, Paris, France
| | - Andreas Goules
- Department of Pathophysiology, School of Medicine, University of Athens, Athens GR15772, Greece
| | - Athanasios G. Tzioufas
- Department of Pathophysiology, School of Medicine, University of Athens, Athens GR15772, Greece
| | - Vassili Soumelis
- INSERM U976, Human Immunology, Physiopathology and Immunotherapy, Paris, France
- Hôpital Saint Louis, Saint Louis Research Institute, Paris, France
| | - Dimitrios I. Fotiadis
- Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina GR45110, Greece
- Department of Biomedical Research, FORTH (Foundation for Research & Technology)-IMBB (Institute of Molecular Biology and Biotechnology), Ioannina GR45110, Greece
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21
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Lee EY, Maloney NJ, Cheng K, Bach DQ. Machine learning for precision dermatology: Advances, opportunities, and outlook. J Am Acad Dermatol 2021; 84:1458-1459. [PMID: 32645400 PMCID: PMC8023050 DOI: 10.1016/j.jaad.2020.06.1019] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 06/08/2020] [Accepted: 06/26/2020] [Indexed: 11/16/2022]
Affiliation(s)
- Ernest Y Lee
- Department of Bioengineering, University of California-Los Angeles; Division of Dermatology, Department of Medicine, University of California-Los Angeles; University of California-Los Angeles-Caltech Medical Scientist Training Program, David Geffen School of Medicine at University of California-Los Angeles.
| | - Nolan J Maloney
- Division of Dermatology, Department of Medicine, University of California-Los Angeles
| | - Kyle Cheng
- Division of Dermatology, Department of Medicine, University of California-Los Angeles
| | - Daniel Q Bach
- Division of Dermatology, Department of Medicine, University of California-Los Angeles
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22
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Tanoli Z, Seemab U, Scherer A, Wennerberg K, Tang J, Vähä-Koskela M. Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Brief Bioinform 2021; 22:1656-1678. [PMID: 32055842 PMCID: PMC7986597 DOI: 10.1093/bib/bbaa003] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/09/2019] [Indexed: 02/07/2023] Open
Abstract
Drug development involves a deep understanding of the mechanisms of action and possible side effects of each drug, and sometimes results in the identification of new and unexpected uses for drugs, termed as drug repurposing. Both in case of serendipitous observations and systematic mechanistic explorations, confirmation of new indications for a drug requires hypothesis building around relevant drug-related data, such as molecular targets involved, and patient and cellular responses. These datasets are available in public repositories, but apart from sifting through the sheer amount of data imposing computational bottleneck, a major challenge is the difficulty in selecting which databases to use from an increasingly large number of available databases. The database selection is made harder by the lack of an overview of the types of data offered in each database. In order to alleviate these problems and to guide the end user through the drug repurposing efforts, we provide here a survey of 102 of the most promising and drug-relevant databases reported to date. We summarize the target coverage and types of data available in each database and provide several examples of how multi-database exploration can facilitate drug repurposing.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
| | - Umair Seemab
- Haartman Institute, University of Helsinki, Finland
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
| | - Krister Wennerberg
- Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Denmark
| | - Jing Tang
- Faculty of medicine, University of Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
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23
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Yu K, Syed MN, Bernardis E, Gelfand JM. Machine Learning Applications in the Evaluation and Management of Psoriasis: A Systematic Review. ACTA ACUST UNITED AC 2021; 5:147-159. [PMID: 33733038 PMCID: PMC7963214 DOI: 10.1177/2475530320950267] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Machine learning (ML), a subset of artificial intelligence (AI) that aims to teach machines to automatically learn tasks by inferring patterns from data, holds significant promise to aid psoriasis care. Applications include evaluation of skin images for screening and diagnosis as well as clinical management including treatment and complication prediction. Objective To summarize literature on ML applications to psoriasis evaluation and management and to discuss challenges and opportunities for future advances. Methods We searched MEDLINE, Google Scholar, ACM Digital Library, and IEEE Xplore for peer-reviewed publications published in English through December 1, 2019. Our search queries identified publications with any of the 10 computing-related keywords and "psoriasis" in the title and/or abstract. Results Thirty-three studies were identified. Articles were organized by topic and synthesized as evaluation- or management-focused articles covering 5 content categories: (A) Evaluation using skin images: (1) identification and differential diagnosis of psoriasis lesions, (2) lesion segmentation, and (3) lesion severity and area scoring; (B) clinical management: (1) prediction of complications and (2) treatment. Conclusion Machine learning has significant potential to aid psoriasis evaluation and management. Current topics popular in ML research on psoriasis are the evaluation of medical images, prediction of complications, and treatment discovery. For patients to derive the greatest benefit from ML advancements, it is helpful for dermatologists to have an understanding of ML and how it can effectively aid their assessments and decision-making.
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Affiliation(s)
- Kimberley Yu
- Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Maha N Syed
- Department of Dermatology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Elena Bernardis
- Department of Dermatology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M Gelfand
- Department of Dermatology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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24
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Shao S, Tsoi LC, Sarkar MK, Xing X, Xue K, Uppala R, Berthier CC, Zeng C, Patrick M, Billi AC, Fullmer J, Beamer MA, Perez-White B, Getsios S, Schuler A, Voorhees JJ, Choi S, Harms P, Kahlenberg JM, Gudjonsson JE. IFN-γ enhances cell-mediated cytotoxicity against keratinocytes via JAK2/STAT1 in lichen planus. Sci Transl Med 2020; 11:11/511/eaav7561. [PMID: 31554739 DOI: 10.1126/scitranslmed.aav7561] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 01/31/2019] [Accepted: 08/02/2019] [Indexed: 12/20/2022]
Abstract
Lichen planus (LP) is a chronic debilitating inflammatory disease of unknown etiology affecting the skin, nails, and mucosa with no current FDA-approved treatments. It is histologically characterized by dense infiltration of T cells and epidermal keratinocyte apoptosis. Using global transcriptomic profiling of patient skin samples, we demonstrate that LP is characterized by a type II interferon (IFN) inflammatory response. The type II IFN, IFN-γ, is demonstrated to prime keratinocytes and increase their susceptibility to CD8+ T cell-mediated cytotoxic responses through MHC class I induction in a coculture model. We show that this process is dependent on Janus kinase 2 (JAK2) and signal transducer and activator of transcription 1 (STAT1), but not JAK1 or STAT2 signaling. Last, using drug prediction algorithms, we identify JAK inhibitors as promising therapeutic agents in LP and demonstrate that the JAK1/2 inhibitor baricitinib fully protects keratinocytes against cell-mediated cytotoxic responses in vitro. In summary, this work elucidates the role and mechanisms of IFN-γ in LP pathogenesis and provides evidence for the therapeutic use of JAK inhibitors to limit cell-mediated cytotoxicity in patients with LP.
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Affiliation(s)
- Shuai Shao
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shannxi 710032, China.,Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lam C Tsoi
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mrinal K Sarkar
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xianying Xing
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ke Xue
- Department of Dermatology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shannxi 710032, China
| | - Ranjitha Uppala
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Celine C Berthier
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chang Zeng
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Matthew Patrick
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Allison C Billi
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Joseph Fullmer
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Maria A Beamer
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Spiro Getsios
- Department of Dermatology, Northwestern University, Chicago, IL 60611, USA
| | - Andrew Schuler
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - John J Voorhees
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sung Choi
- Pediatric Hematology/Oncology, Mott Children's Hospital, University of Michigan, Ann Arbor, MI 48109, USA
| | - Paul Harms
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Pathology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - J Michelle Kahlenberg
- Division of Rheumatology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Johann E Gudjonsson
- Department of Dermatology, University of Michigan, Ann Arbor, MI 48109, USA.
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25
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Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155135] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.
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26
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Chan S, Reddy V, Myers B, Thibodeaux Q, Brownstone N, Liao W. Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations. Dermatol Ther (Heidelb) 2020; 10:365-386. [PMID: 32253623 PMCID: PMC7211783 DOI: 10.1007/s13555-020-00372-0] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Indexed: 12/14/2022] Open
Abstract
Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.
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Affiliation(s)
- Stephanie Chan
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Vidhatha Reddy
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Bridget Myers
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Quinn Thibodeaux
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Nicholas Brownstone
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA
| | - Wilson Liao
- Department of Dermatology, University of California San Francisco, San Francisco, CA, USA.
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27
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Sonthalia S, Agrawal M, Sehgal VN, Gandhi V, Gupta KS. Drugs, discovery, and dermatology: Renbök, research and repurposing. Drug Discov Today 2019; 25:259-262. [PMID: 31678676 DOI: 10.1016/j.drudis.2019.10.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 10/17/2019] [Accepted: 10/25/2019] [Indexed: 11/28/2022]
Affiliation(s)
| | - Mahima Agrawal
- Department of Dermatology & STD, Lady Hardinge Medical College & SK Hospital, New Delhi, India
| | - Virendra N Sehgal
- Dermato-Venereology (Skin/VD) Centre, Sehgal Nursing Home, New Delhi, India
| | - Vijay Gandhi
- Department of Dermatology & STD, University College of Medical Sciences & GTB Hospital, New Delhi, India
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