Publications
An Investigational Modeling Approach for Improving Gene Selection using Regularized Cox Regression Model
May 27, 2023Journal Mathematical Biology and Bioinformatics
Publisher Ghada Yousif Ismail Abdallha and Zakariya Yahya Algamal
DOI https://doi.org/10.17537/2023.18.282
Issue 282-293
Volume 18
By producing the required proteins, the process of gene expression establishes the physical properties of living things. Gene expression from DNA or RNA may be recorded using a variety of approaches. Regression analysis has evolved in prominence in the area of genetic research recently. Several of the genes in high dimensional gene expression information for statistical inference may not be related to their illnesses, which is one of the major problems. The ability of gene selection to enhance the outcomes of several techniques has been demonstrated. For censored survival data, the Cox proportional hazards regression model is the most widely used model. In order to identify important genes and achieve high classification accuracy, a new technique for selecting the tuning parameter is suggested in this study using an optimization algorithm. According to experimental findings, the suggested strategy performs much better than the two rival methods in terms of the area under the curve and the number of chosen genes. This study provides a comprehensive assessment of the latest work on performance evaluation of regression analysis in gene selection. In addition to its performance analysis, this research conducts a thorough assessment of the numerous efforts done on various extended models based on gene selection in recent years.
A QSAR classification model of skin sensitization potential based on improving binary crow search algorithm.
May 2, 2020Journal Electronic Journal of Applied Statistical Analysis.
Publisher Ghada Yousif Ismail Abdallha and Zakariya Yahya Algamal
DOI 10.1285/i20705948v13n1p86
Issue 01
Volume 13
Classifying of skin sensitization using the quantitative structure-activity relationship (QSAR) model is important. Applying descriptor selection is essential to improve the performance of the classification task. Recently, a binary crow search algorithm (BCSA) was proposed, which has been suc- cessfully applied to solve variable selection. In this work, a new time-varying transfer function is proposed to improve the exploration and exploitation capability of the BCSA in selecting the most relevant descriptors in QSAR classification model with high classification accuracy and short computing time. The results demonstrate that the proposed method is reliable and can reasonably separate the compounds according to sensitizers or non-sensitizers with high classification accuracy.
Variable selection in Poisson regression model using invasive weed optimization algorithm.
Dec 1, 2019Journal Iraqi Journal of Statistical Sciences
Publisher Ghada Yousif Ismail Abdallha and Zakariya Yahya Algamal
DOI https://doi.org/10.33899/iqjoss.2019.164173
Issue 39-54
Volume 16
Variable selection is a very helpful procedure for improving prediction accuracy by finding the most important variables that are related to the response variable. Poisson regression model has received much attention in several science fields for modeling count data. Invasive weed optimization algorithm (IWO) is one of the recently efficient proposed nature-inspired algorithms that can efficiently be employed for variable selection. In this work, IWO algorithm is proposed to perform variable selection for Poisson regression model. Extensive simulation studies and real data application are conducted to evaluate the performance of the proposed method in terms of prediction accuracy and variable selection criteria. The results proved the efficiency of our proposed methods and it outperforms other popular methods.