Browsing by Author "Pashaei, Elnaz"

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    An Efficient Binary Sand Cat Swarm Optimization for Feature Selection in High-Dimensional Biomedical Data
    (MDPI, 2023-09-25) Pashaei, Elnaz; Medical and Molecular Genetics, School of Medicine
    Recent breakthroughs are making a significant contribution to big data in biomedicine which are anticipated to assist in disease diagnosis and patient care management. To obtain relevant information from this data, effective administration and analysis are required. One of the major challenges associated with biomedical data analysis is the so-called “curse of dimensionality”. For this issue, a new version of Binary Sand Cat Swarm Optimization (called PILC-BSCSO), incorporating a pinhole-imaging-based learning strategy and crossover operator, is presented for selecting the most informative features. First, the crossover operator is used to strengthen the search capability of BSCSO. Second, the pinhole-imaging learning strategy is utilized to effectively increase exploration capacity while avoiding premature convergence. The Support Vector Machine (SVM) classifier with a linear kernel is used to assess classification accuracy. The experimental results show that the PILC-BSCSO algorithm beats 11 cutting-edge techniques in terms of classification accuracy and the number of selected features using three public medical datasets. Moreover, PILC-BSCSO achieves a classification accuracy of 100% for colon cancer, which is difficult to classify accurately, based on just 10 genes. A real Liver Hepatocellular Carcinoma (TCGA-HCC) data set was also used to further evaluate the effectiveness of the PILC-BSCSO approach. PILC-BSCSO identifies a subset of five marker genes, including prognostic biomarkers HMMR, CHST4, and COL15A1, that have excellent predictive potential for liver cancer using TCGA data.
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    CDHu40: a novel marker gene set of neuroendocrine prostate cancer
    (Oxford University Press, 2024) Liu, Sheng; Nam, Hye Seung; Zeng, Ziyu; Deng, Xuehong; Pashaei, Elnaz; Zang, Yong; Yang, Lei; Li, Chenglong; Huang, Jiaoti; Wendt, Michael K.; Lu, Xin; Huang, Rong; Wan, Jun; Medical and Molecular Genetics, School of Medicine
    Prostate cancer (PCa) is the most prevalent cancer affecting American men. Castration-resistant prostate cancer (CRPC) can emerge during hormone therapy for PCa, manifesting with elevated serum prostate-specific antigen levels, continued disease progression, and/or metastasis to the new sites, resulting in a poor prognosis. A subset of CRPC patients shows a neuroendocrine (NE) phenotype, signifying reduced or no reliance on androgen receptor signaling and a particularly unfavorable prognosis. In this study, we incorporated computational approaches based on both gene expression profiles and protein-protein interaction networks. We identified 500 potential marker genes, which are significantly enriched in cell cycle and neuronal processes. The top 40 candidates, collectively named CDHu40, demonstrated superior performance in distinguishing NE PCa (NEPC) and non-NEPC samples based on gene expression profiles. CDHu40 outperformed most of the other published marker sets, excelling particularly at the prognostic level. Notably, some marker genes in CDHu40, absent in the other marker sets, have been reported to be associated with NEPC in the literature, such as DDC, FOLH1, BEX1, MAST1, and CACNA1A. Importantly, elevated CDHu40 scores derived from our predictive model showed a robust correlation with unfavorable survival outcomes in patients, indicating the potential of the CDHu40 score as a promising indicator for predicting the survival prognosis of those patients with the NE phenotype. Motif enrichment analysis on the top candidates suggests that REST and E2F6 may serve as key regulators in the NEPC progression.