ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2017
A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.
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Who cites it
71 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning techniques for cancer classification using microarray gene expression data.Frontiers in physiology · 2022Pooled it
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- Using transfer learning approaches to predict RNA-Seq gene expression data for cancer classification.Frontiers in artificial intelligence · 2026Article
- Towards a new taxonomy of preterm birth.Journal of perinatology : official journal of the California Perinatal Association · 2025Review
- Breast Cancer Detection Using Convolutional Neural Networks: A Deep Learning-Based Approach.Cureus · 2025Review
- Microbial Metabolites-induced Epigenetic Modifications for Inhibition of Colorectal Cancer: Current Status and Future Perspectives.Mini reviews in medicinal chemistry · 2025Review
- Bayesian-optimized deep learning for identifying essential genes of mitophagy and fostering therapies to combat drug resistance in human cancers.Journal of cellular and molecular medicine · 2025Article
- Navigating the Multiverse: a Hitchhiker's guide to selecting harmonization methods for multimodal biomedical data.Biology methods & protocols · 2025Review
- Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics.Scientific reports · 2024Article
- Characterizing diseases using genetic and clinical variables: A data analytics approach.Quantitative biology (Beijing, China) · 2024Article
- Integrative analysis of RNA expression data unveils distinct cancer types through machine learning techniques.Saudi journal of biological sciences · 2024Article
- Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data.PloS one · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- GAN-based data augmentation for transcriptomics: survey and comparative assessment.Bioinformatics (Oxford, England) · 2023Article
- A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women.Diagnostics (Basel, Switzerland) · 2023Article
- Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning.Biophysics reports · 2023Article
- RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data.Journal of biological engineering · 2023Article
- Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.Diagnostics (Basel, Switzerland) · 2023Review
- Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy.Frontiers in oncology · 2023Article
- Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach.Journal of multidisciplinary healthcare · 2023Review
11 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used Stacked Denoising Autoencoder (SDAE) to deeply extract functional features from high dimensional gene expression profiles. Next, we evaluated the performance of the extracted representation through supervised classification models to verify the usefulness of the new features in cancer detection. Lastly, we identified a set of highly interactive genes by analyzing the SDAE connectivity matrices. Our results and analysis illustrate that these highly interactive genes could be useful cancer biomarkers for the detection of breast cancer that deserve further studies.
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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 Socratic graph.