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Updated: Feb 10, 2026

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
Published on: December 27, 2010
THD-Tricluster: A robust triclustering technique and its application in condition specific change analysis in HIV-1
Tulika Kakati1, Hasin A Ahmed1, Dhruba K Bhattacharyya1
1Department of Computer Science and Engineering, School of Engineering, Tezpur University, Napaam, Sonitpur, Assam 784028, India.
Abstract:
Developing a cost-effective and robust triclustering algorithm that can identify triclusters of high biological significance in the gene-sample-time (GST) domain is a challenging task. Most existing triclustering algorithms can detect shifting and scaling patterns in isolation, they are not able to handle co-occurring shifting-and-scaling patterns. This paper makes an attempt to address this issue. It introduces a robust triclustering algorithm called THD-Tricluster to identify triclusters over the GST domain. In addition to applying over several benchmark datasets for its validation, the proposed THD-Tricluster algorithm was applied on HIV-1 progression data to identify disease-specific genes. THD-Tricluster could identify 38 most responsible genes for the deadly disease which includes GATA3, EGR1, JUN, ELF1, AGFG1, AGFG2, CX3CR1, CXCL12, CCR5, CCR2, and many others. The results are validated using GeneCard and other established results.
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