ArticleBioMed research international2023
Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods.
Article in BioMed research international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning.Biochemical genetics · 2024Article
- Machine Learning in Identifying Marker Genes for Congenital Heart Diseases of Different Cardiac Cell Types.Life (Basel, Switzerland) · 2024Article
- Machine Learning Reveals Impacts of Smoking on Gene Profiles of Different Cell Types in Lung.Life (Basel, Switzerland) · 2024Article
- Identification of key gene expression associated with quality of life after recovery from COVID-19.Medical & biological engineering & computing · 2024Article
- PredictEFC: a fast and efficient multi-label classifier for predicting enzyme family classes.BMC bioinformatics · 2024Article
- Patterns of Gene Expression Profiles Associated with Colorectal Cancer in Colorectal Mucosa by Using Machine Learning Methods.Combinatorial chemistry & high throughput screening · 2024Article
- Identification of Colon Immune Cell Marker Genes Using Machine Learning Methods.Life (Basel, Switzerland) · 2023Article
- Identification of Gene Markers Associated with COVID-19 Severity and Recovery in Different Immune Cell Subtypes.Biology · 2023Article
- Identification of Phase-Separation-Protein-Related Function Based on Gene Ontology by Using Machine Learning Methods.Life (Basel, Switzerland) · 2023Article
- Machine Learning Classification of Time since BNT162b2 COVID-19 Vaccination Based on Array-Measured Antibody Activity.Life (Basel, Switzerland) · 2023Article
- Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells.Life (Basel, Switzerland) · 2023Article
- Identification of Genes Associated with the Impairment of Olfactory and Gustatory Functions in COVID-19 via Machine-Learning Methods.Life (Basel, Switzerland) · 2023Article
- Identification of genes related to immune enhancement caused by heterologous ChAdOx1-BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods.Frontiers in immunology · 2023Article
- Immune responses of different COVID-19 vaccination strategies by analyzing single-cell RNA sequencing data from multiple tissues using machine learning methods.Frontiers in genetics · 2023Article
- Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.Frontiers in microbiology · 2023Article
- Characterization of chromatin accessibility patterns in different mouse cell types using machine learning methods at single-cell resolution.Frontiers in genetics · 2023Article
Corrections and comments
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Long-term cigarette smoking causes various human diseases, including respiratory disease, cancer, and gastrointestinal (GI) disorders. Alterations in gene expression and variable splicing processes induced by smoking are associated with the development of diseases. This study applied advanced machine learning methods to identify the isoforms with important roles in distinguishing smokers from former smokers based on the expression profile of isoforms from current and former smokers collected in one previous study. These isoforms were deemed as features, which were first analyzed by the Boruta to select features highly correlated with the target variables. Then, the selected features were evaluated by four feature ranking algorithms, resulting in four feature lists. The incremental feature selection method was applied to each list for obtaining the optimal feature subsets and building high-performance classification models. Furthermore, a series of classification rules were accessed by decision tree with the highest performance. Eventually, the rationality of the mined isoforms (features) and classification rules was verified by reviewing previous research. Features such as isoforms ENST00000464835 (expressed by LRRN3), ENST00000622663 (expressed by SASH1), and ENST00000284311 (expressed by GPR15), and pathways (cytotoxicity mediated by natural killer cell and cytokine-cytokine receptor interaction) revealed by the enrichment analysis, were highly relevant to smoking response, suggesting the robustness of our analysis pipeline.
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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.