ArticleLife (Basel, Switzerland)2022
Predicting Heart Cell Types by Using Transcriptome Profiles and a Machine Learning Method.
Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 44 citations in OpenAlex.
- Transcriptomic Approaches to Cardiomyocyte-Biomaterial Interactions: A Review.ACS biomaterials science & engineering · 2024Review
- Implications for neutrophils in cardiac arrhythmias.American journal of physiology. Heart and circulatory physiology · 2024Review
- Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data.Interdisciplinary sciences, computational life sciences · 2024Article
- Exploration of neuron heterogeneity in human heart failure with dilated cardiomyopathy through single-cell RNA sequencing analysis.BMC cardiovascular disorders · 2024Article
- Review
- Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods.BioMed research international · 2023Article
- Identification of Transcriptome Biomarkers for Severe COVID-19 with Machine Learning Methods.Biomolecules · 2022Article
- Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods.Life (Basel, Switzerland) · 2022Article
- Screening gene signatures for clinical response subtypes of lung transplantation.Molecular genetics and genomics : MGG · 2022Article
- Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method.Life (Basel, Switzerland) · 2022Article
- Identification of Cell Markers and Their Expression Patterns in Skin Based on Single-Cell RNA-Sequencing Profiles.Life (Basel, Switzerland) · 2022Article
- Identification of uveitis-associated functions based on the feature selection analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment scores.Frontiers in molecular neuroscience · 2022Article
- Identification of protein-protein interaction associated functions based on gene ontology and KEGG pathway.Frontiers in genetics · 2022Article
- Exploring the Genomic Patterns in Human and Mouse Cerebellums Via Single-Cell Sequencing and Machine Learning Method.Frontiers in genetics · 2022Article
- Functional and embedding feature analysis for pan-cancer classification.Frontiers in oncology · 2022Article
- Identifying anal and cervical tumorigenesis-associated methylation signaling with machine learning methods.Frontiers in oncology · 2022Article
- Characterization of spleen and lymph node cell typesFrontiers in molecular neuroscience · 2022Article
- Identification of COVID-19 severity biomarkers based on feature selection on single-cell RNA-Seq data of CD8Frontiers in genetics · 2022Article
- Identification of Methylation Signatures and Rules for Sarcoma Subtypes by Machine Learning Methods.BioMed research international · 2022Article
- Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer's Disease.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
9 authors at 7 institutions in 1 country.
Funding
Abstract
The heart is an essential organ in the human body. It contains various types of cells, such as cardiomyocytes, mesothelial cells, endothelial cells, and fibroblasts. The interactions between these cells determine the vital functions of the heart. Therefore, identifying the different cell types and revealing the expression rules in these cell types are crucial. In this study, multiple machine learning methods were used to analyze the heart single-cell profiles with 11 different heart cell types. The single-cell profiles were first analyzed via light gradient boosting machine method to evaluate the importance of gene features on the profiling dataset, and a ranking feature list was produced. This feature list was then brought into the incremental feature selection method to identify the best features and build the optimal classifiers. The results suggested that the best decision tree (DT) and random forest classification models achieved the highest weighted F1 scores of 0.957 and 0.981, respectively. The selected features, such as NPPA, LAMA2, DLC1, and the classification rules extracted from the optimal DT classifier played a crucial role in cardiac structure and function in recent research and enrichment analysis. In particular, some lncRNAs (LINC02019, NEAT1) were found to be quite important for the recognition of different cardiac cell types. In summary, these findings provide a solid academic foundation for the development of molecular diagnostics and biomarker discovery for cardiac diseases.
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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.