Search research articles

Search research articles

Related Experiment Videos

CLIFF: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts.

E P Xing¹, R M Karp

¹Division of Computer Science, University of California, Berkeley, Berkeley, CA 94720, USA. epxing@cs.berkeley.edu

Bioinformatics (Oxford, England)

|July 27, 2001

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

Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with polynomial functions.

Heredity·2017

Same author

An inhibitor of methionine aminopeptidase type-2, PPI-2458, ameliorates the pathophysiological disease processes of rheumatoid arthritis.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]·2008

Same author

Reconstructing chain functions in genetic networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing·2004

Same author

Automatic discovery of sub-molecular sequence domains in multi-aligned sequences: a dynamic programming algorithm for multiple alignment segmentation.

Journal of theoretical biology·2001

Same author

An algorithm combining discrete and continuous methods for optical mapping.

Journal of computational biology : a journal of computational molecular cell biology·2001

Same author

Estrogen receptor-beta expression in relation to the expression of luteinizing hormone receptor and cytochrome P450 enzymes in rat ovarian follicles.

Biology of reproduction·2000

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026

Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026

Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026

Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026

Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026

See all related articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

CLIFF, a novel algorithm for clustering biological samples, effectively addresses challenges in gene expression data. It integrates feature selection with clustering to improve sample classification accuracy.

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Clustering biological samples using gene expression microarray data presents challenges due to data sparsity, high dimensionality, and irrelevant/redundant features.
Existing clustering approaches often do not adequately address feature selection, potentially impacting accuracy.

Purpose of the Study:

To introduce CLIFF, an iterative algorithm designed for robust clustering of biological samples from gene expression data.
To enhance clustering performance by integrating a sophisticated feature filtering process.

Main Methods:

CLIFF employs an iterative approach, alternating between feature filtering and clustering.
Feature filtering ranks genes based on discriminability, relevance to a reference partition, and irredundancy.

Related Experiment Videos

Clustering is performed using a normalized cut method on the selected features.

Main Results:

CLIFF demonstrated superior performance compared to standard clustering methods lacking feature selection on a leukemia sample dataset.
The algorithm achieved a clustering result closely matching expert labeling for 72 leukemia samples across 7130 genes.

Conclusions:

CLIFF offers an effective solution for clustering biological samples, particularly when dealing with high-dimensional and sparse gene expression data.
The integration of iterative feature selection significantly improves clustering accuracy and reliability.