ArticleCurrent gene therapy2025
DeepLCRmiRNA: A Hybrid Neural Network Approach for Identifying Lung Cancer-Associated miRNAs.
Article in Current gene therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.Annals of medicine · 2026Article
- Frequency-domain multi-scale hybrid attention for pathological image classification.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionLung cancer stands as one of the most prevalent malignant neoplasms, with microRNAs (miRNAs) playing a pivotal role in the modulation of gene expression, impacting cancer cell proliferation, invasion, metastasis, immune escape, and resistance to therapy.
methodsThe intricate role of miRNAs in lung cancer underscores their significance as biomarkers for early detection and as novel targets for therapeutic intervention. Traditional approaches for the identification of miRNAs related to lung cancer, however, are impeded by inefficiencies and complexities.
resultsIn response to these challenges, this study introduced an innovative deep-learning strategy designed for the efficient and precise identification of lung cancer-associated miRNAs. Through comprehensive benchmark tests, our method exhibited superior performance relative to existing technologies.
conclusionFurther case studies have also confirmed the ability of our model to identify lung cancer- associated miRNAs that have undergone biological validation.
Indexed as
Identifiers
39279703What 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.