Machine learning and multi-omics in precision medicine for ME/CFS
Abstract Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and multifaceted disorder that defies simplistic characterisation. Traditional approaches to diagnosing and treating ME/CFS have often fallen short due to the condition’s heterogeneity and the lack of validated biomark...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-024-05915-z |
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| author | Katherine Huang Brett A. Lidbury Natalie Thomas Paul R. Gooley Christopher W. Armstrong |
| author_facet | Katherine Huang Brett A. Lidbury Natalie Thomas Paul R. Gooley Christopher W. Armstrong |
| author_sort | Katherine Huang |
| collection | DOAJ |
| description | Abstract Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and multifaceted disorder that defies simplistic characterisation. Traditional approaches to diagnosing and treating ME/CFS have often fallen short due to the condition’s heterogeneity and the lack of validated biomarkers. The growing field of precision medicine offers a promising approach which focuses on the genetic and molecular underpinnings of individual patients. In this review, we explore how machine learning and multi-omics (genomics, transcriptomics, proteomics, and metabolomics) can transform precision medicine in ME/CFS research and healthcare. We provide an overview on machine learning concepts for analysing large-scale biological data, highlight key advancements in multi-omics biomarker discovery, data quality and integration strategies, while reflecting on ME/CFS case study examples. We also highlight several priorities, including the critical need for applying robust computational tools and collaborative data-sharing initiatives in the endeavour to unravel the biological intricacies of ME/CFS. |
| format | Article |
| id | doaj-art-85b3aa830ac843a98bcf25ed8194f823 |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-85b3aa830ac843a98bcf25ed8194f8232025-01-19T12:37:12ZengBMCJournal of Translational Medicine1479-58762025-01-0123111410.1186/s12967-024-05915-zMachine learning and multi-omics in precision medicine for ME/CFSKatherine Huang0Brett A. Lidbury1Natalie Thomas2Paul R. Gooley3Christopher W. Armstrong4Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of MelbourneThe National Centre for Epidemiology and Population Health, ANU College of Health and Medicine, The Australian National UniversityDepartment of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of MelbourneDepartment of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of MelbourneDepartment of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of MelbourneAbstract Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and multifaceted disorder that defies simplistic characterisation. Traditional approaches to diagnosing and treating ME/CFS have often fallen short due to the condition’s heterogeneity and the lack of validated biomarkers. The growing field of precision medicine offers a promising approach which focuses on the genetic and molecular underpinnings of individual patients. In this review, we explore how machine learning and multi-omics (genomics, transcriptomics, proteomics, and metabolomics) can transform precision medicine in ME/CFS research and healthcare. We provide an overview on machine learning concepts for analysing large-scale biological data, highlight key advancements in multi-omics biomarker discovery, data quality and integration strategies, while reflecting on ME/CFS case study examples. We also highlight several priorities, including the critical need for applying robust computational tools and collaborative data-sharing initiatives in the endeavour to unravel the biological intricacies of ME/CFS.https://doi.org/10.1186/s12967-024-05915-zPrecision medicineMulti-omicsBiomarkersMachine learningArtificial intelligenceData integration |
| spellingShingle | Katherine Huang Brett A. Lidbury Natalie Thomas Paul R. Gooley Christopher W. Armstrong Machine learning and multi-omics in precision medicine for ME/CFS Journal of Translational Medicine Precision medicine Multi-omics Biomarkers Machine learning Artificial intelligence Data integration |
| title | Machine learning and multi-omics in precision medicine for ME/CFS |
| title_full | Machine learning and multi-omics in precision medicine for ME/CFS |
| title_fullStr | Machine learning and multi-omics in precision medicine for ME/CFS |
| title_full_unstemmed | Machine learning and multi-omics in precision medicine for ME/CFS |
| title_short | Machine learning and multi-omics in precision medicine for ME/CFS |
| title_sort | machine learning and multi omics in precision medicine for me cfs |
| topic | Precision medicine Multi-omics Biomarkers Machine learning Artificial intelligence Data integration |
| url | https://doi.org/10.1186/s12967-024-05915-z |
| work_keys_str_mv | AT katherinehuang machinelearningandmultiomicsinprecisionmedicineformecfs AT brettalidbury machinelearningandmultiomicsinprecisionmedicineformecfs AT nataliethomas machinelearningandmultiomicsinprecisionmedicineformecfs AT paulrgooley machinelearningandmultiomicsinprecisionmedicineformecfs AT christopherwarmstrong machinelearningandmultiomicsinprecisionmedicineformecfs |