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Updated: Feb 8, 2026

Screening Foodstuffs for Class 1 Integrons and Gene Cassettes

Screening Foodstuffs for Class 1 Integrons and Gene Cassettes

Published on: June 19, 2015

AUCTSP: an improved biomarker gene pair class predictor.

Dimitri Kagaris¹, Alireza Khamesipour², Constantin T Yiannoutsos³

¹Department of Electrical and Computer Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, 62901, IL, USA. kagaris@engr.siu.edu.

BMC Bioinformatics

|June 27, 2018

Summary

A new AUCTSP classifier improves gene expression classification by analyzing gene pair rankings across all subjects, outperforming the original Top Scoring Pair (TSP) method. This approach enhances accuracy and reduces the selection of non-informative genes.

Keywords:

AUC Breast cancer Colon cancer Diffuse large B-Cell lymphoma Gene expression Gene selection Leukemia Microarray data analysis Ovarian cancer Prostate cancer Receiver operating characteristic (ROC) curve

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

The Top Scoring Pair (TSP) classifier uses gene expression ranking reversals for disease classification.
However, TSP overlooks crucial information and can select non-differential
pivot

Purpose of the Study:

Introduce the AUCTSP classifier as an improved rank-based method for gene expression classification.
Enhance classification accuracy and robustness compared to the original TSP.

Main Methods:

Developed the AUCTSP classifier utilizing the Area Under the ROC Curve (AUC) to estimate gene pair ranking reversals.
Applied AUCTSP to diverse cancer datasets (ovarian, leukemia, colon, breast, prostate) and lymphoma.

Main Results:

AUCTSP demonstrates superior classification accuracy across multiple cancer types.
The method effectively avoids overfitting and the selection of non-informative pivot genes.
AUCTSP leverages the separation of gene expression distributions across all subjects.

Conclusions:

AUCTSP offers a reliable and robust rank-based approach for gene expression classification.
By considering relative gene rankings across all subjects, AUCTSP achieves significant performance gains over TSP.
The AUCTSP method is less prone to selecting non-informative genes, improving classifier reliability.