ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2024
intCC: An efficient weighted integrative consensus clustering of multimodal data.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- Revolutionizing multi-omics analysis with artificial intelligence and data processing.Quantitative biology (Beijing, China) · 2025Review
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Authors and funding
2 authors.
Funding
Abstract
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.
Indexed as
Identifiers
38160311PMC10764072What OpenQuestion holds
Registered trials
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