ReviewComputational and structural biotechnology journal2021
Machine learning and deep learning methods that use omics data for metastasis prediction.
Review in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 1 of them a synthesis that pooled it.
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.
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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
92 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.Cells · 2025Pooled it
- Influenza-Like Illness Forecasting Using Multisource Data: Comparative Deep Learning Study.JMIR medical informatics · 2026Article
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- Importance ranking and predictive model construction of WMSDs risk factors among shift-working nurses based on multiple machine-learning algorithms.BMC nursing · 2026Article
- High-throughput strategy for targeting MDM2 in uveal melanoma to reverse radiation therapy resistance.Cell death discovery · 2026Article
- Research on features selection of medical diagnostic models based on L-S-ACO algorithm.iScience · 2026Article
- METTL3-mediated GLUD1 m6A Modification Promotes Hydrogen Peroxide-induced Mitochondrial Dysfunction in Human Nucleus Pulposus Cells Via the Glutamate/α-KG Metabolic Axis.Journal of musculoskeletal & neuronal interactions · 2026Article
- Multi-omics analysis identifies macrophage immunometabolic signatures in ulcerative colitis.Frontiers in cellular and infection microbiology · 2026Article
- Machine learning model predicts clotting risk during CRRT in ESKD patients: a SHAP-interpretable approach.Renal failure · 2025Article
- Pan-Cancer Landscape of Magnesium Homeostasis: Bulk Omics Research and Single-Cell Sequencing Validation.Biological procedures online · 2025Article
- Integrating traditional Chinese pulse diagnosis with machine learning: novel approaches for pregnancy and coronary heart disease identification.Scientific reports · 2025Article
- Deep Learning-Based Mpox Skin Lesion Detection and Real-Time Monitoring in a Smart Healthcare System.Diagnostics (Basel, Switzerland) · 2025Article
- Brain Stroke Classification Using CT Scans with Transformer-Based Models and Explainable AI.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Blood-brain barrier-penetrating Angiopep-2/Sirtuin 1 nanoparticles rescue sevoflurane neurotoxicity through multi-omics identified necroptosis pathways.Journal of nanobiotechnology · 2025Article
- From lab to life: technological innovations in transforming cancer metastasis detection and therapy.Discover oncology · 2025Review
- Development and validation of a new diagnostic prediction model for NAFLD based on machine learning algorithms in NHANES 2017-2020.3.Hormones (Athens, Greece) · 2025Article
- Predictive modeling for metastasis in oncology: current methods and future directions.Annals of medicine and surgery (2012) · 2025Review
- Integrated machine learning survival framework develops a prognostic model based on macrophage-related genes and programmed cell death signatures in a multi-sample Kidney renal clear cell carcinoma.Cell biology and toxicology · 2025Article
- Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.BMC gastroenterology · 2025Article
32 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.