ReviewInternational journal of molecular sciences2022
Personalized Risk Schemes and Machine Learning to Empower Genomic Prognostication Models in Myelodysplastic Syndromes.
Review in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.Molecular medicine (Cambridge, Mass.) · 2025Review
- Machine learning prediction of the failure of high-flow nasal oxygen therapy in patients with acute respiratory failure.Scientific reports · 2024Article
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- IPSS-M in myelodysplastic neoplasms arising from aplastic anemia and paroxysmal nocturnal hemoglobinuria.Blood · 2023Article
- Molecular Aspects of Hematological Malignancies and Benign Hematological Disorders.International journal of molecular sciences · 2023Article
- Molecular patterns identify distinct subclasses of myeloid neoplasia.Nature communications · 2023Article
- How I Manage Transplant Ineligible Patients with Myelodysplastic Neoplasms.Clinical hematology international · 2023Review
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- The Use of Machine Learning Techniques to Predict Deep Vein Thrombosis in Rehabilitation Inpatients.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Myelodysplastic syndromes (MDS) are characterized by variable clinical manifestations and outcomes. Several prognostic systems relying on clinical factors and cytogenetic abnormalities have been developed to help stratify MDS patients into different risk categories of distinct prognoses and therapeutic implications. The current abundance of molecular information poses the challenges of precisely defining patients' molecular profiles and their incorporation in clinically established diagnostic and prognostic schemes. Perhaps the prognostic power of the current systems can be boosted by incorporating molecular features. Machine learning (ML) algorithms can be helpful in developing more precise prognostication models that integrate complex genomic interactions at a higher dimensional level. These techniques can potentially generate automated diagnostic and prognostic models and assist in advancing personalized therapies. This review highlights the current prognostication models used in MDS while shedding light on the latest achievements in ML-based research.
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Registered trials
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