ArticleInternational journal of molecular sciences2024
Machine Learning-Based Characterization and Identification of Tertiary Lymphoid Structures Using Spatial Transcriptomics Data.
Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 3 citations in OpenAlex.
- Review
- Tertiary lymphoid structure topotypes in intrahepatic cholangiocarcinoma: spatial immunity, checkpoint-response associations, and therapeutic reprogramming.Frontiers in immunology · 2026Review
- Tertiary lymphoid structures in chronic rhinosinusitis with nasal polyps: spatial organization, pathological functions, and implications for surgical strategy and biologic therapy.Frontiers in immunology · 2026Review
- Single-Cell Transcriptomics: Technical Advances, Applications and Challenges in Cancer Drug Discovery.Current cancer drug targets · 2025Article
- Dissecting Tertiary Lymphoid Structures in Cancer: Maturation, Localization and Density.Theranostics · 2025Review
- Role of tertiary lymphoid structures and B cells in clinical immunotherapy of gastric cancer.Frontiers in immunology · 2024Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Tertiary lymphoid structures (TLSs) are organized aggregates of immune cells in non-lymphoid tissues and are associated with a favorable prognosis in tumors. However, TLS markers remain inconsistent, and the utilization of machine learning techniques for this purpose is limited. To tackle this challenge, we began by identifying TLS markers through bioinformatics analysis and machine learning techniques. Subsequently, we leveraged spatial transcriptomic data from Gene Expression Omnibus (GEO) and built two support vector classifier models for TLS prediction: one without feature selection and the other using the marker genes. The comparable performances of these two models confirm the efficacy of the selected markers. The majority of the markers are immunoglobulin genes, demonstrating their importance in the identification of TLSs. Our research has identified the markers of TLSs using machine learning methods and constructed a model to predict TLS location, contributing to the detection of TLS and holding the promising potential to impact cancer treatment strategies.
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What OpenQuestion holds
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.