ArticleJournal of personalized medicine2022
Machine-Learning-Based Late Fusion on Multi-Omics and Multi-Scale Data for Non-Small-Cell Lung Cancer Diagnosis.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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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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Who cites it
29 citing papers in PubMed.
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- RPSLearner: A Novel Approach Based on Random Projection and Deep Stacking Learning for Categorizing Non-Small Cell Lung Cancer.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2026Article
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- A fusion-based multiomics classification approach for enhanced gene discovery in non-small cell lung cancer.Bioinformatics advances · 2026Article
- Multimodal artificial intelligence and machine learning in oncology: from data integration to precision cancer care.Frontiers in digital health · 2026Review
- Nanoparticle-Based Tools to Study Hallmarks of Aging at the Molecular Level.International journal of nanomedicine · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
- Integration of ROSE cytology and serum tumor markers for rapid subtyping of lung cancer.Scientific reports · 2025Article
- A robust machine learning model based on ribosomal-subunit-derived piRNAs for diagnostic potential of nonsmall cell lung cancer across multicentre, large-scale of sequencing data.Clinical and translational medicine · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- A review of hybrid EEG-based multimodal human-computer interfaces using deep learning: applications, advances, and challenges.Biomedical engineering letters · 2025Review
- Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer.Nature reviews. Cancer · 2025Review
- Benchmarking ensemble machine learning algorithms for multi-class, multi-omics data integration in clinical outcome prediction.Briefings in bioinformatics · 2025Article
- Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models.Nature biomedical engineering · 2025Article
- Smart nanoplatforms for early detection and immune modulation in lung cancer.Frontiers in bioengineering and biotechnology · 2025Review
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
- Multimodality Fusion Aspects of Medical Diagnosis: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Deep learning-based approaches for multi-omics data integration and analysis.BioData mining · 2024Review
- Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine.Journal of personalized medicine · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Differentiation between the various non-small-cell lung cancer subtypes is crucial for providing an effective treatment to the patient. For this purpose, machine learning techniques have been used in recent years over the available biological data from patients. However, in most cases this problem has been treated using a single-modality approach, not exploring the potential of the multi-scale and multi-omic nature of cancer data for the classification. In this work, we study the fusion of five multi-scale and multi-omic modalities (RNA-Seq, miRNA-Seq, whole-slide imaging, copy number variation, and DNA methylation) by using a late fusion strategy and machine learning techniques. We train an independent machine learning model for each modality and we explore the interactions and gains that can be obtained by fusing their outputs in an increasing manner, by using a novel optimization approach to compute the parameters of the late fusion. The final classification model, using all modalities, obtains an F1 score of 96.81±1.07, an AUC of 0.993±0.004, and an AUPRC of 0.980±0.016, improving those results that each independent model obtains and those presented in the literature for this problem. These obtained results show that leveraging the multi-scale and multi-omic nature of cancer data can enhance the performance of single-modality clinical decision support systems in personalized medicine, consequently improving the diagnosis of the patient.
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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 Socratic graph.