ReviewBiomedicines2021
Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research.
Review in Biomedicines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Progress in the Application of Machine Learning in the Field of Single-Cell and Spatial Transcriptomics.Genes · 2026Review
- Predicting Macrophage Spatial Localization from Single-Cell Transcriptomes to Uncover Disease Mechanisms.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.Briefings in bioinformatics · 2025Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- Machine Learning-Derived Neddylation Gene Signature for Predicting Prognosis and Immunotherapy Benefits in Colorectal Cancer.ImmunoTargets and therapy · 2025Article
- RNA Metabolism and the Role of Small RNAs in Regulating Multiple Aspects of RNA Metabolism.Non-coding RNA · 2024Review
- A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics.BMC genomics · 2023Article
- Enhancing single-cell biology through advanced AI-powered microfluidics.Biomicrofluidics · 2023Article
- Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information.Experimental hematology & oncology · 2022Review
- Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.Briefings in bioinformatics · 2022Review
- Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning.Biomedicines · 2022Article
- Intercellular Communication-Related Molecular Subtypes and a Gene Signature Identified by the Single-Cell RNA Sequencing Combined with a Transcriptomic Analysis.Disease markers · 2022Article
- Cochlear Development; New Tools and Approaches.Frontiers in cell and developmental biology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.
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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.