ArticlePatterns (New York, N.Y.)2021
Artificial image objects for classification of schizophrenia with GWAS-selected SNVs and convolutional neural network.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- ImmuGT-ConRes: a consistency learning residual network for predicting pan-cancer immunotherapy response from gene expression data.NPJ precision oncology · 2026Article
- GRPa-PRS: A risk stratification method to identify genetically-regulated pathways in polygenic diseases.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Visualization of Runs of Homozygosity and Classification Using Convolutional Neural Networks.Biology · 2025Article
- Article
- Modeling Chickpea Productivity with Artificial Image Objects and Convolutional Neural Network.Plants (Basel, Switzerland) · 2024Article
- Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification.PNAS nexus · 2024Article
- Convolutional neural network for biomarker discovery for triple negative breast cancer with RNA sequencing data.Heliyon · 2023Article
- Modeling of Flowering Time inPlants (Basel, Switzerland) · 2022Article
- AggMapNet: enhanced and explainable low-sample omics deep learning with feature-aggregated multi-channel networks.Nucleic acids research · 2022Article
- Artificial image objects for classification of breast cancer biomarkers with transcriptome sequencing data and convolutional neural network algorithms.Breast cancer research : BCR · 2021Article
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
Abstract
In this article, we propose a new approach to analyze large genomics data. We considered individual genetic variants as pixels in an image and transformed a collection of variants into an artificial image object (AIO), which could be classified as a regular image by CNN algorithms. Using schizophrenia as a case study, we demonstrate the principles and their applications with 3 datasets. With 4,096 SNVs, the CNN models achieved an accuracy of 0.678 ± 0.007 and an AUC of 0.738 ± 0.008 for the diagnosis phenotype. With 44,100 SNVs, the models achieved class-specific accuracies of 0.806 ± 0.032 and 0.820 ± 0.049, and AUCs of 0.930 ± 0.017 and 0.867 ± 0.040 for the bottom and top classes stratified by the patient's polygenic risk scores. These results suggest that, once transformed to images, large genomics data can be analyzed effectively with image classification algorithms.
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.