ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2015
Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
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- Towards a new taxonomy of preterm birth.Journal of perinatology : official journal of the California Perinatal Association · 2025Review
- MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder.BioData mining · 2024Article
- Machine Learning and Health Science Research: Tutorial.Journal of medical Internet research · 2024Article
- Developing and comparing deep learning and machine learning algorithms for osteoporosis risk prediction.Frontiers in artificial intelligence · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Identifying prognostic subgroups of luminal-A breast cancer using deep autoencoders and gene expressions.PLoS computational biology · 2023Article
- Identification of gene profiles related to the development of oral cancer using a deep learning technique.BMC medical genomics · 2023Article
- Differential Private Deep Learning Models for Analyzing Breast Cancer Omics Data.Frontiers in oncology · 2022Article
- AIME: Autoencoder-based integrative multi-omics data embedding that allows for confounder adjustments.PLoS computational biology · 2022Article
- Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder.Frontiers in oncology · 2022Article
- Deep learning-based transcriptome model predicts survival of T-cell acute lymphoblastic leukemia.Frontiers in oncology · 2022Article
- A Knowledge-Based Discovery Approach Couples Artificial Neural Networks With Weight Engineering to Uncover Immune-Related Processes Underpinning Clinical Traits of Breast Cancer.Frontiers in immunology · 2022Article
- i-Modern: Integrated multi-omics network model identifies potential therapeutic targets in glioma by deep learning with interpretability.Computational and structural biotechnology journal · 2022Article
- Deep Learning-Based Protein Features Predict Overall Survival and Chemotherapy Benefit in Gastric Cancer.Frontiers in oncology · 2022Article
- A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.International journal of molecular sciences · 2021Review
- MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks.Genome biology · 2021Article
- Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data.Cancers · 2021Article
- Denoising large-scale biological data using network filters.BMC bioinformatics · 2021Article
1 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
Big data bring new opportunities for methods that efficiently summarize and automatically extract knowledge from such compendia. While both supervised learning algorithms and unsupervised clustering algorithms have been successfully applied to biological data, they are either dependent on known biology or limited to discerning the most significant signals in the data. Here we present denoising autoencoders (DAs), which employ a data-defined learning objective independent of known biology, as a method to identify and extract complex patterns from genomic data. We evaluate the performance of DAs by applying them to a large collection of breast cancer gene expression data. Results show that DAs successfully construct features that contain both clinical and molecular information. There are features that represent tumor or normal samples, estrogen receptor (ER) status, and molecular subtypes. Features constructed by the autoencoder generalize to an independent dataset collected using a distinct experimental platform. By integrating data from ENCODE for feature interpretation, we discover a feature representing ER status through association with key transcription factors in breast cancer. We also identify a feature highly predictive of patient survival and it is enriched by FOXM1 signaling pathway. The features constructed by DAs are often bimodally distributed with one peak near zero and another near one, which facilitates discretization. In summary, we demonstrate that DAs effectively extract key biological principles from gene expression data and summarize them into constructed features with convenient properties.
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25592575PMC4299935What 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.