ArticleHeliyon2022
Identification by genetic algorithm optimized back propagation artificial neural network and validation of a four-gene signature for diagnosis and prognosis of pancreatic cancer.
Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 4 citations in OpenAlex.
- Analysis of frailty status and its influencing factors in maintenance hemodialysis patients based on the health ecological model.BMC nephrology · 2025Article
- Article
- Article
- Genetically Optimized Modular Neural Networks for Precision Lung Cancer Diagnosis: Exploratory Study of Novel Approach.Cancer diagnosis & prognosisArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Although some improvements in the management of pancreatic cancer (PC) have been made, no major breakthroughs in terms of biomarker discovery or effective treatment have emerged. Here, we applied artificial intelligence (AI)-based methods to develop a model to diagnose PC and predict survival outcome. Methods: Multiple bioinformatics methods, including Limma Package, were performed to identify differentially expressed genes (DEGs) in PC. A Back Propagation (BP) model was constructed, followed by Genetic Algorithm (GA) filtering and verification of its prognosis capacity in the TCGA cohort. Furthermore, we validated the protein expression of the selected DEGs in 92 clinical PC tissues using immunohistochemistry. Finally, intro studies were performed to assess the function of Results: Four candidate genes ( Conclusions: Using Limma Package and GA-ANN, we developed and validated a diagnostic and prognostic gene signature that yielded excellent predictive capacity for PC patients' survival. In vitro studies were further conducted to verify the functions of
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.