Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Non-linear cancer classification using a modified radial basis function classification algorithm.

Hong-Qiang Wang1, De-Shuang Huang

  • 1Intelligent Computation Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Science, P.O. Box 1130, , Hefei, Anhui, 230031, China. hqwang@iim.ac.cn

Journal of Biomedical Science
|September 1, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Human-Structure-Aware Token Position Embedding for Tokenized Pose Estimation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

GraphLooper: predicting chromatin loops based on hierarchical multi-view graph pooling method.

Briefings in bioinformatics·2026
Same author

LMSCDA: A Secondary Structure Enhanced Language Model for Predicting CircRNA and Disease Associations.

IEEE journal of biomedical and health informatics·2026
Same author

Multi-species integration, alignment and annotation of single-cell RNA-seq data with CAMEX.

Nature communications·2026
Same author

SpaLSTF: Diffusion-based generative model with BiLSTM and XCA-Transformer for spatial transcriptomics imputation.

PLoS computational biology·2026
Same author

A scalable computational framework for predicting gene expression from candidate <i>cis</i>-regulatory elements.

Genome research·2026
Same journal

CLEC5A/TLR2 bispecific antibody suppresses dengue virus-induced pro-inflammatory cytokines production from macrophages.

Journal of biomedical science·2026
Same journal

Offense and defense: itaconate mediates bidirectional immune regulation of host-bacteria interaction.

Journal of biomedical science·2026
Same journal

Enhanced expression of ADAMTS1 in ovarian carcinomas: loss of ADAMTS1 expression instigates cellular reprogramming of extracellular matrix ensuing altered plasticity, augmented migration and attenuated adhesion.

Journal of biomedical science·2026
Same journal

Mechanobiology of the tumor microenvironment: a review of therapeutic interactions and in vitro elasticity measurement techniques.

Journal of biomedical science·2026
Same journal

Lack of cortistatin drives neuroimmune and vascular dysfunction in brain ischemia.

Journal of biomedical science·2026
Same journal

ENT1 inhibitor J4 restores cognitive function and white-matter integrity in a mouse model of tuberous sclerosis complex.

Journal of biomedical science·2026
See all related articles

This study introduces a novel radial basis function (RBF) algorithm for accurate non-linear cancer classification using gene expression data. The enhanced RBF classifier demonstrates superior performance in distinguishing tumor types.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Oncology

Background:

  • Accurate cancer classification from gene expression profiles is crucial for diagnosis and treatment.
  • Existing classification algorithms face challenges in handling the non-linear nature of biological data.
  • Radial basis function (RBF) networks offer a powerful framework for non-linear classification tasks.

Purpose of the Study:

  • To develop and evaluate a modified radial basis function (RBF) classification algorithm for improved non-linear cancer classification.
  • To optimize the RBF classifier structure using a combination of simulated annealing, linear least square, and gradient methods.
  • To validate the algorithm's efficacy on real-world gene expression datasets for different human tumor classes.

Main Methods:

Related Experiment Videos

  • A modified simulated annealing approach was integrated with linear least square and gradient paradigms.
  • The optimization focused on enhancing the structure of the radial basis function (RBF) classifier.
  • The algorithm was applied to two distinct microarray datasets: Normal vs. colon tumor and acute myeloid leukemia (AML) vs. acute lymphoblastic leukemia (ALL).

Main Results:

  • The proposed modified RBF algorithm achieved accurate non-linear classification for cancer types.
  • The algorithm demonstrated robust performance on both tested microarray datasets.
  • Comparative analysis showed the enhanced accuracy and stability of the proposed method over existing algorithms.

Conclusions:

  • The modified RBF classification algorithm provides an effective tool for non-linear cancer classification using gene expression data.
  • The integration of modified simulated annealing offers a superior method for RBF classifier optimization.
  • This approach holds promise for advancing cancer diagnostics through precise classification of tumor subtypes.