ReviewZhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences2024
Advances in applications of artificial intelligence algorithms for cancer-related miRNA research.
Review in Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- MicroRNAs in endometriosis: bioinformatics resources, machine learning strategies, and multi-omics perspectives.Journal of translational medicine · 2026Review
- A New Frontier in Phytotherapy: Harnessing the Therapeutic Power of Medicinal Herb-derived miRNAs.Current pharmaceutical design · 2024Review
- Decoding the Promise and Challenges of miRNA-Based Cancer Therapies: An Essential Update on miR-21, miR-34, and miR-155.International journal of medical sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.
Indexed as
Identifiers
What Socratic 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 Socratic graph.