ArticleBMC genomics2017
A deep auto-encoder model for gene expression prediction.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.PLoS computational biology · 2026Article
- Decoding the significance of TERT gene in Polycystic Ovary Syndrome: From molecular mechanisms to emerging diagnostic and therapeutic strategies.Journal of ovarian research · 2026Review
- A hybrid feature extraction framework combining PCA and mutual information for gene expression based lung cancer classification.PloS one · 2026Article
- ProGraphTrans: multimodal dynamic collaborative framework for protein representation learning.BMC biology · 2025Article
- Integrative bioinformatics and deep learning to identify common genetic pathways in Crohn's disease and ischemic cardiomyopathy.Journal, genetic engineering & biotechnology · 2025Article
- Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Can Loneliness be Predicted? Development of a Risk Prediction Model for Loneliness among Elderly Chinese: A Study Based on CLHLS.Research square · 2024Article
- Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.Nucleic acids research · 2024Article
- Current understanding of functional peptides encoded by lncRNA in cancer.Cancer cell international · 2024Review
- Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.Journal of chemical information and modeling · 2024Article
- AutoTransOP: translating omics signatures without orthologue requirements using deep learning.NPJ systems biology and applications · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Deep learning-empowered crop breeding: intelligent, efficient and promising.Frontiers in plant science · 2023Article
- Identification of monotonically expressed long non-coding RNA signatures for breast cancer using variational autoencoders.PloS one · 2023Article
- Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy.Frontiers in oncology · 2023Article
- Deep Learning in Diverse Intelligent Sensor Based Systems.Sensors (Basel, Switzerland) · 2022Review
- NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data.BMC medicine · 2022Article
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000-2021].Frontiers in genetics · 2022Article
- Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundGene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance.
resultsTo demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns.
conclusionWe provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.
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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.