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Related Experiment Video

Updated: Jun 18, 2026

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment
06:32

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

Published on: August 18, 2023

Tumor Classification Using High-Order Gene Expression Profiles Based on Multilinear ICA.

Ming-Gang Du1, Shan-Wen Zhang, Hong Wang

  • 1School of Urban and Environment Science, Shanxi Normal University, Linfen, Shanxi 041004, China.

Advances in Bioinformatics
|December 4, 2009
PubMed
Summary
This summary is machine-generated.

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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA (lncRNA)...

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Multilinear-ICA, a novel method, enhances Independent Components Analysis (ICA) for high-order gene expression profiles (GEP) tumor classification. This approach effectively distinguishes tumor subtypes using tensor analysis and Support Vector Machine (SVM) classification.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning

Background:

  • Independent Components Analysis (ICA) is limited in analyzing high-order gene expression profiles (GEP) and discerning relationships between factors.
  • Existing methods struggle with the complexity of high-order GEP data mining for tumor classification.

Purpose of the Study:

  • To generalize ICA for high-order GEP data by introducing Multilinear-ICA.
  • To apply Multilinear-ICA and Support Vector Machine (SVM) for effective tumor classification using high-order GEP.

Main Methods:

  • Introduced tensor concepts and operations, alongside Multilinear-ICA and SVM classifier.
  • Selected high-scoring genes from GEP using t-statistics and tabulated tensors.
  • Applied Multilinear-ICA to the processed tensors for feature extraction.

Related Experiment Videos

Last Updated: Jun 18, 2026

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment
06:32

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

Published on: August 18, 2023

Main Results:

  • Demonstrated the effectiveness and feasibility of the proposed Multilinear-ICA and SVM method on three high-order GEP datasets.
  • Achieved successful tumor subtype classification, validating the approach.

Conclusions:

  • Multilinear-ICA offers a generalized and effective approach for high-order GEP tumor classification.
  • The study provides insights for developing more advanced tumor classification algorithms.