ReviewJournal of biological methods2024
Cancer genetics and deep learning applications for diagnosis, prognosis, and categorization.
Review in Journal of biological methods, 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.
- Application of Artificial Intelligence in Stem Cells and Gene Therapy for Gynecological Cancers.Current stem cell research & therapy · 2026Review
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- AI-based prediction of drug-gene interactions modulating tight junction integrity: A deep learning framework highlighting multiple therapeutic targets.Journal of oral biology and craniofacial researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene expression data are used to discover meaningful hidden information in gene datasets. Cancer and other disorders may be diagnosed based on differences in gene expression profiles, and this information can be gleaned by gene sequencing. Thanks to the tremendous power of artificial intelligence (AI), healthcare has become a significant user of deep learning (DL) for predicting cancer diseases and categorizing gene expression. Gene expression Microarrays have been proved effective in predicting cancer diseases and categorizing gene expression. Gene expression datasets contain only limited samples, but the features of cancer are diverse and complex. To overcome their dimensionality, gene expression datasets must be enhanced. By learning and analyzing features of input data, it is possible to extract features, as multidimensional arrays, from the data. Synthetic samples are needed to strengthen the range of information. DL strategies may be used when gene expression data are used to diagnose and classify cancer diseases.
Indexed as
Identifiers
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.