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Proposing a Robust Discriminant Analysis Method with Application to Genetic Sequence Classification Using GenBank Data | ||
| AL-Qadisiyah Journal For Administrative and Economic sciences | ||
| Volume 28, Issue 1, April 2026, Pages 134-139 PDF (701.5 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.33916/qjae.2026.01134139 | ||
| Authors | ||
| Mohammed H. AL-Sharoot; Souada bdel husseinmusa | ||
| University of AL-Qadisiya | ||
| Abstract | ||
| This research proposes a robust discriminant analysis method for classifying genetic sequences related to breast cancer, using real genomic data obtained from GenBank. Traditional discriminant methods such as Fisher’s Linear Discriminant Function and Sherrod’s model are known to be sensitive to outliers, which are common in genomic datasets due to biological variability and sequencing errors. To overcome this, the proposed method integrates robust estimators of location and scale along with a novel reweighting algorithm that reduces the influence of outlying gene expressions. The model’s performance is evaluated through a simulation study under clean and contaminated conditions, showing improved classification accuracy and reduced misclassification rates. For real data application, genetic sequences of breast tissue samples labeled as tumor and normal were analyzed. The robust model achieved superior accuracy in separating cancerous from non-cancerous samples, confirming its practical value in biomedical classification tasks involving noisy or high-variability data. | ||
| Keywords | ||
| Robust Discriminant Analysis; Breast Cancer; Genetic Sequences; Outliers; GenBank; Tumor Classification | ||
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