ArticleScientific reports2019
DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06124989 (A Novel Machine Learning Model for the Prediction of Relapse of Acute Biliary Pancreatitis), which is not on this map. Cited by 112 papers, 2 of them syntheses that pooled it.
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.
A Novel Machine Learning Model for the Prediction of Relapse of Acute Biliary Pancreatitis (Machine learnINg for the rElapse Risk eValuation in Acute Biliary Pancreatitis - MINERVA)
Who cites it
112 citing papers in PubMed, 2 syntheses or guidelines pooled it, 405 citations in OpenAlex.
- Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide.Frontiers in bioengineering and biotechnology · 2023Pooled it
- On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature.BMC medical informatics and decision making · 2022Pooled it
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- Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging.Biosensors · 2026Review
- Transforming multi-omics data into images for disease classification: A review of techniques and tools.Journal of pathology informatics · 2026Review
- An Interpretable Omics-to-Image Transformer Framework for Cancer Prognosis Prediction.Computational and structural biotechnology journal · 2026Article
- Glioma identification from microRNA biomarkers using machine learning.Frontiers in systems biology · 2026Article
- Image generator for tabular data based on non-Euclidean metrics for CNN-based classification.PloS one · 2026Article
- CDR-Net: A computerized framework to detect Alzheimer's diseases and mild cognitive impairment.PloS one · 2026Article
- SubNExT: Towards accurate, efficient and robust gene expression classification for breast cancer subtyping.Computational and structural biotechnology journal · 2026Article
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Patient classification using single-cell RNA-sequencing data with deep learning convolutional neural networks.Frontiers in bioinformatics · 2026Article
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- SynergyImage: image-based model for drug combinations synergy score prediction.BMC bioinformatics · 2025Article
52 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is critical, but difficult, to catch the small variation in genomic or other kinds of data that differentiates phenotypes or categories. A plethora of data is available, but the information from its genes or elements is spread over arbitrarily, making it challenging to extract relevant details for identification. However, an arrangement of similar genes into clusters makes these differences more accessible and allows for robust identification of hidden mechanisms (e.g. pathways) than dealing with elements individually. Here we propose, DeepInsight, which converts non-image samples into a well-organized image-form. Thereby, the power of convolution neural network (CNN), including GPU utilization, can be realized for non-image samples. Furthermore, DeepInsight enables feature extraction through the application of CNN for non-image samples to seize imperative information and shown promising results. To our knowledge, this is the first work to apply CNN simultaneously on different kinds of non-image datasets: RNA-seq, vowels, text, and artificial.
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.