intCC: An Efficient Weighted Integrative Consensus Clustering of Multimodal Data

Details

• Personal Author:
  Huang C ; Kuan PF
• Description:
  High throughput profiling of multiomics data provides a valuable resource to better understand the complex human disease such as cancer and to potentially uncover new subtypes. Integrative clustering has emerged as a powerful unsupervised learning framework for subtype discovery. In this paper, we propose an efficient weighted integrative clustering called intCC by combining ensemble method, consensus clustering and kernel learning integrative clustering. We illustrate that intCC can accurately uncover the latent cluster structures via extensive simulation studies and a case study on the TCGA pan cancer datasets. An R package intCC implementing our proposed method is available at https://github.com/candsj/intCC. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Algorithms Author Keywords: Integrative Clustering Bioinformatics Consensus Clustering DNA Damage Ensemble Learning Genomics Molecular Epidemiology Multiomics Data Simulation Methods
• ISBN:
  9789811286414
• Publisher:
  World Scientific Publishing
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Proceedings
• Place as Subject:
  New York ; OSHA Region 2
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• NIOSHTIC Number:
  nn:20069251
• Citation:
  Pacific Symposium on Biocomputing 2024, January 3-7, 2024, Kohala Coast, Hawaii. Altman RB, Hunter L, Ritchie MD, Murray T, Klein TE, eds. Hackensack, NJ: World Scientific Publishing, 2023 Dec; :627-640
• Contact Point Address:
  Pei Fen Kuan, Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
• Email:
  eifen.kuan@stonybrook.edu
• Editor(s):
  Altman RB; Hunter L; Ritchie MD; Murray T; Klein TE
• Federal Fiscal Year:
  2024
• Performing Organization:
  State University New York Stony Brook
• Peer Reviewed:
  False
• Start Date:
  20210701
• Source Full Name:
  Pacific Symposium on Biocomputing 2024, January 3-7, 2024, Kohala Coast, Hawaii
• End Date:
  20240630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0893e740cac21fc086560452b252fb4352eca13b3beb16b53130620360d6c53ae00d0ab7a525241c09433cdb73209cb466fe370b419ffa14ec32b90706b538f5
• Download URL:
  https://stacks.cdc.gov/view/cdc/207759/cdc_207759_DS1.pdf
• File Type:
  [PDF - 2.09 MB ]