skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Automated exploitation of deep learning for cancer patient stratification across multiple types

Bioinformatics (Oxford, England), 2023-11, Vol.39 (11) [Peer Reviewed Journal]

The Author(s) 2023. Published by Oxford University Press. 2023 ;ISSN: 1367-4803 ;EISSN: 1367-4811 ;DOI: 10.1093/bioinformatics/btad654 ;PMID: 37934154

Full text available

Citations Cited by
  • Title:
    Automated exploitation of deep learning for cancer patient stratification across multiple types
  • Author: Sun, Pingping ; Fan, Shijie ; Li, Shaochuan ; Zhao, Yingwei ; Lu, Chang ; Wong, Ka-Chun ; Li, Xiangtao
  • Lu, Zhiyong
  • Subjects: Original Paper
  • Is Part Of: Bioinformatics (Oxford, England), 2023-11, Vol.39 (11)
  • Description: Abstract Motivation Recent frameworks based on deep learning have been developed to identify cancer subtypes from high-throughput gene expression profiles. Unfortunately, the performance of deep learning is highly dependent on its neural network architectures which are often hand-crafted with expertise in deep neural networks, meanwhile, the optimization and adjustment of the network are usually costly and time consuming. Results To address such limitations, we proposed a fully automated deep neural architecture search model for diagnosing consensus molecular subtypes from gene expression data (DNAS). The proposed model uses ant colony algorithm, one of the heuristic swarm intelligence algorithms, to search and optimize neural network architecture, and it can automatically find the optimal deep learning model architecture for cancer diagnosis in its search space. We validated DNAS on eight colorectal cancer datasets, achieving the average accuracy of 95.48%, the average specificity of 98.07%, and the average sensitivity of 96.24%, respectively. Without the loss of generality, we investigated the general applicability of DNAS further on other cancer types from different platforms including lung cancer and breast cancer, and DNAS achieved an area under the curve of 95% and 96%, respectively. In addition, we conducted gene ontology enrichment and pathological analysis to reveal interesting insights into cancer subtype identification and characterization across multiple cancer types. Availability and implementation The source code and data can be downloaded from https://github.com/userd113/DNAS-main. And the web server of DNAS is publicly accessible at 119.45.145.120:5001.
  • Publisher: Oxford University Press
  • Language: English
  • Identifier: ISSN: 1367-4803
    EISSN: 1367-4811
    DOI: 10.1093/bioinformatics/btad654
    PMID: 37934154
  • Source: Oxford Journals Open Access Collection
    Journals@Ovid Open Access Journal Collection Rolling
    PubMed Central
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait