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Gomes LGDS, Cruz ÁASD, de Santana MBR, Pinheiro GP, Santana CVN, Santos CBS, Boorgula MP, Campbell M, Machado ADS, Veiga RV, Barnes KC, Costa RDS, Figueiredo CA. Predictive genetic panel for adult asthma using machine learning methods. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. GLOBAL 2024; 3:100282. [PMID: 38952894 PMCID: PMC11215340 DOI: 10.1016/j.jacig.2024.100282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 02/20/2024] [Accepted: 04/05/2024] [Indexed: 07/03/2024]
Abstract
Background Asthma is a chronic inflammatory disease of the airways that is heterogeneous and multifactorial, making its accurate characterization a complex process. Therefore, identifying the genetic variations associated with asthma and discovering the molecular interactions between the omics that confer risk of developing this disease will help us to unravel the biological pathways involved in its pathogenesis. Objective We sought to develop a predictive genetic panel for asthma using machine learning methods. Methods We tested 3 variable selection methods: Boruta's algorithm, the top 200 genome-wide association study markers according to their respective P values, and an elastic net regression. Ten different algorithms were chosen for the classification tests. A predictive panel was built on the basis of joint scores between the classification algorithms. Results Two variable selection methods, Boruta and genome-wide association studies, were statistically similar in terms of the average accuracies generated, whereas elastic net had the worst overall performance. The predictive genetic panel was completed with 155 single-nucleotide variants, with 91.18% accuracy, 92.75% sensitivity, and 89.55% specificity using the support vector machine algorithm. The markers used range from known single-nucleotide variants to those not previously described in the literature. Our study shows potential in creating genetic prediction panels with tailored penalties per marker, aiding in the identification of optimal machine learning methods for intricate results. Conclusions This method is able to classify asthma and nonasthma effectively, proving its potential utility in clinical prediction and diagnosis.
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Affiliation(s)
| | | | | | | | - Cinthia Vila Nova Santana
- Programa de Controle da Asma na Bahia (ProAR), Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | | | | | - Monica Campbell
- Department of Medicine, University of Colorado Denver, Aurora, Colo
| | - Adelmir de Souza Machado
- Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia, Brazil
- Programa de Controle da Asma na Bahia (ProAR), Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - Rafael Valente Veiga
- Laboratory of Lymphocyte Signalling and Development, The Babraham Institute, Cambridge, United Kingdom
| | | | - Ryan dos Santos Costa
- Instituto de Ciências da Saúde, Universidade Federal da Bahia, Salvador, Bahia, Brazil
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Aljarallah NA, Dutta AK, Sait ARW. A Systematic Review of Genetics- and Molecular-Pathway-Based Machine Learning Models for Neurological Disorder Diagnosis. Int J Mol Sci 2024; 25:6422. [PMID: 38928128 PMCID: PMC11203850 DOI: 10.3390/ijms25126422] [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: 04/24/2024] [Revised: 05/29/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
The process of identification and management of neurological disorder conditions faces challenges, prompting the investigation of novel methods in order to improve diagnostic accuracy. In this study, we conducted a systematic literature review to identify the significance of genetics- and molecular-pathway-based machine learning (ML) models in treating neurological disorder conditions. According to the study's objectives, search strategies were developed to extract the research studies using digital libraries. We followed rigorous study selection criteria. A total of 24 studies met the inclusion criteria and were included in the review. We classified the studies based on neurological disorders. The included studies highlighted multiple methodologies and exceptional results in treating neurological disorders. The study findings underscore the potential of the existing models, presenting personalized interventions based on the individual's conditions. The findings offer better-performing approaches that handle genetics and molecular data to generate effective outcomes. Moreover, we discuss the future research directions and challenges, emphasizing the demand for generalizing existing models in real-world clinical settings. This study contributes to advancing knowledge in the field of diagnosis and management of neurological disorders.
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Affiliation(s)
- Nasser Ali Aljarallah
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia;
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh 13713, Saudi Arabia;
| | - Abdul Rahaman Wahab Sait
- Department of Documents and Archive, Center of Documents and Administrative Communication, King Faisal University, Al-Ahsa, Al Hofuf 31982, Saudi Arabia
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Omenn GS, Lane L, Overall CM, Lindskog C, Pineau C, Packer NH, Cristea IM, Weintraub ST, Orchard S, Roehrl MHA, Nice E, Guo T, Van Eyk JE, Liu S, Bandeira N, Aebersold R, Moritz RL, Deutsch EW. The 2023 Report on the Proteome from the HUPO Human Proteome Project. J Proteome Res 2024; 23:532-549. [PMID: 38232391 PMCID: PMC11026053 DOI: 10.1021/acs.jproteome.3c00591] [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] [Indexed: 01/19/2024]
Abstract
Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.
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Affiliation(s)
- Gilbert S. Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and University of Geneva, 1015 Lausanne, Switzerland
| | - Christopher M. Overall
- University of British Columbia, Vancouver, BC V6T 1Z4, Canada, Yonsei University Republic of Korea
| | | | - Charles Pineau
- University Rennes, Inserm U1085, Irset, 35042 Rennes, France
| | | | | | - Susan T. Weintraub
- University of Texas Health Science Center-San Antonio, San Antonio, Texas 78229-3900, United States
| | | | - Michael H. A. Roehrl
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, United States
| | | | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory, Westlake University, Hangzhou 310024, Zhejiang Province, China
| | - Jennifer E. Van Eyk
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 South San Vicente Boulevard, Pavilion, 9th Floor, Los Angeles, CA, 90048, United States
| | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, CA, 92093, United States
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology in ETH Zurich, 8092 Zurich, Switzerland
- University of Zurich, 8092 Zurich, Switzerland
| | - Robert L. Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W. Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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Chang CC, Liu TC, Lu CJ, Chiu HC, Lin WN. Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis. Front Microbiol 2023; 14:1227300. [PMID: 37829445 PMCID: PMC10565662 DOI: 10.3389/fmicb.2023.1227300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/06/2023] [Indexed: 10/14/2023] Open
Abstract
Myasthenia gravis (MG) is a neuromuscular junction disease with a complex pathophysiology and clinical variation for which no clear biomarker has been discovered. We hypothesized that because changes in gut microbiome composition often occur in autoimmune diseases, the gut microbiome structures of patients with MG would differ from those without, and supervised machine learning (ML) analysis strategy could be trained using data from gut microbiota for diagnostic screening of MG. Genomic DNA from the stool samples of MG and those without were collected and established a sequencing library by constructing amplicon sequence variants (ASVs) and completing taxonomic classification of each representative DNA sequence. Four ML methods, namely least absolute shrinkage and selection operator, extreme gradient boosting (XGBoost), random forest, and classification and regression trees with nested leave-one-out cross-validation were trained using ASV taxon-based data and full ASV-based data to identify key ASVs in each data set. The results revealed XGBoost to have the best predicted performance. Overlapping key features extracted when XGBoost was trained using the full ASV-based and ASV taxon-based data were identified, and 31 high-importance ASVs (HIASVs) were obtained, assigned importance scores, and ranked. The most significant difference observed was in the abundance of bacteria in the Lachnospiraceae and Ruminococcaceae families. The 31 HIASVs were used to train the XGBoost algorithm to differentiate individuals with and without MG. The model had high diagnostic classification power and could accurately predict and identify patients with MG. In addition, the abundance of Lachnospiraceae was associated with limb weakness severity. In this study, we discovered that the composition of gut microbiomes differed between MG and non-MG subjects. In addition, the proposed XGBoost model trained using 31 HIASVs had the most favorable performance with respect to analyzing gut microbiomes. These HIASVs selected by the ML model may serve as biomarkers for clinical use and mechanistic study in the future. Our proposed ML model can identify several taxonomic markers and effectively discriminate patients with MG from those without with a high accuracy, the ML strategy can be applied as a benchmark to conduct noninvasive screening of MG.
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Affiliation(s)
- Che-Cheng Chang
- PhD Program in Nutrition and Food Science, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Neurology, Fu Jen Catholic University Hospital, Fu Jen Catholic University, New Taipei City, Taiwan
- Graduate Institute of Biomedical and Pharmaceutical Science, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Tzu-Chi Liu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Chi-Jie Lu
- Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City, Taiwan
- Artificial Intelligence Development Center, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Hou-Chang Chiu
- School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
- Department of Neurology, Taipei Medical University, Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Wei-Ning Lin
- Graduate Institute of Biomedical and Pharmaceutical Science, Fu Jen Catholic University, New Taipei City, Taiwan
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Huang EJC, Wu MH, Wang TJ, Huang TJ, Li YR, Lee CY. Myasthenia Gravis: Novel Findings and Perspectives on Traditional to Regenerative Therapeutic Interventions. Aging Dis 2023; 14:1070-1092. [PMID: 37163445 PMCID: PMC10389825 DOI: 10.14336/ad.2022.1215] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/15/2022] [Indexed: 05/12/2023] Open
Abstract
The prevalence of myasthenia gravis (MG), an autoimmune disorder, is increasing among all subsets of the population leading to an elevated economic and social burden. The pathogenesis of MG is characterized by the synthesis of autoantibodies against the acetylcholine receptor (AChR), low-density lipoprotein receptor-related protein 4 (LRP4), or muscle-specific kinase at the neuromuscular junction, thereby leading to muscular weakness and fatigue. Based on clinical and laboratory examinations, the research is focused on distinguishing MG from other autoimmune, genetic diseases of neuromuscular transmission. Technological advancements in machine learning, a subset of artificial intelligence (AI) have been assistive in accurate diagnosis and management. Besides, addressing the clinical needs of MG patients is critical to improving quality of life (QoL) and satisfaction. Lifestyle changes including physical exercise and traditional Chinese medicine/herbs have also been shown to exert an ameliorative impact on MG progression. To achieve enhanced therapeutic efficacy, cholinesterase inhibitors, immunosuppressive drugs, and steroids in addition to plasma exchange therapy are widely recommended. Under surgical intervention, thymectomy is the only feasible alternative to removing thymoma to overcome thymoma-associated MG. Although these conventional and current therapeutic approaches are effective, the associated adverse events and surgical complexity limit their wide application. Moreover, Restivo et al. also, to increase survival and QoL, further recent developments revealed that antibody, gene, and regenerative therapies (such as stem cells and exosomes) are currently being investigated as a safer and more efficacious alternative. Considering these above-mentioned points, we have comprehensively reviewed the recent advances in pathological etiologies of MG including COVID-19, and its therapeutic management.
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Affiliation(s)
- Evelyn Jou-Chen Huang
- Department of Ophthalmology, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Ophthalmology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Meng-Huang Wu
- Department of Orthopedics, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Orthopaedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Tsung-Jen Wang
- Department of Ophthalmology, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Ophthalmology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Tsung-Jen Huang
- Department of Orthopedics, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Orthopaedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Yan-Rong Li
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital and College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Ching-Yu Lee
- Department of Orthopedics, Taipei Medical University Hospital, Taipei, Taiwan.
- Department of Orthopaedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
- International PhD Program for Cell Therapy and Regeneration Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.
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