ArticleBMC bioinformatics2017
A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.Inflammatory bowel diseases · 2022Pooled it
- Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease.Frontiers in immunology · 2025Review
- The Use of Machine Learning in the Diagnosis of Kidney Allograft Rejection: Current Knowledge and Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Systems Approaches to Cell Culture-Derived Extracellular Vesicles for Acute Kidney Injury Therapy: Prospects and Challenges.Function (Oxford, England) · 2024Review
- The effect of non-linear signal in classification problems using gene expression.PLoS computational biology · 2023Article
- MetaSEM: Gene Regulatory Network Inference from Single-Cell RNA Data by Meta-Learning.International journal of molecular sciences · 2023Article
- STNet: A novel spiking neural network combining its own time signal with the spatial signal of an artificial neural network.Frontiers in neuroscience · 2023Article
- Scalable transcriptomics analysis with Dask: applications in data science and machine learning.BMC bioinformatics · 2022Review
- Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods.Scientific reports · 2022Article
- Bias-invariant RNA-sequencing metadata annotation.GigaScience · 2021Article
- Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data.BMC bioinformatics · 2021Article
- Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions.World journal of gastroenterology · 2021Review
- Incorporating biological structure into machine learning models in biomedicine.Current opinion in biotechnology · 2020Review
- Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.BMC bioinformatics · 2020Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Transcriptome-Based Prediction of Complex Traits in Maize.The Plant cell · 2020Article
- A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network.BMC medical genomics · 2019Article
- Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response.Immuno-oncology technology · 2019Review
- Integrating Gene Expression Data Into Genomic Prediction.Frontiers in genetics · 2019Article
- Shallow Sparsely-Connected Autoencoders for Gene Set Projection.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundStratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model.
resultsWe benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database.
conclusionIn summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.
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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 OpenQuestion graph.