A Modified Ant Colony Optimization Algorithm for Tumor Marker Gene Selection

<div id="titleAndAbstract"><p class="0abstract">Breast cancer poses the greatest threat to human life and especially to women's life. Despite the progress made in data mining technology in recent years, the ability to predict and diagnose such fatal diseases based on gene expression data still reveals a limited prediction performance, which may not be surprising since most of the genes in expression data are believed to be irrelevant or redundant. The dimensionality reduction process may be considered as a crucial step to analyze gene expression data, as it can reduce the high dimensionality of the breast cancer datasets, which may result into a better prediction performance of such diseases. The paper suggests a new hybrid approach-based gene selection that combines the filter method and the Ant Colony Optimization algorithm to find the smallest subset of informative genes (genes markers) among 24,481 genes. The proposed approach combines four machine learning algorithms - C5.0 Decision Tree, Support Vector Machines, K-Nearest Neighbors algorithm, and Random Forest Classifier - to classify each of the selected samples (patients) into two classes which have cancer or not. Compared with existing methods in the literature, experimental results indicate that our proposed gene selection approach achieved globally higher classification accuracies with a relatively smaller number of genes.</p></div>

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Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

Finance and Credit ◽

10.24891/fc.26.11.2427 ◽

2020 ◽

Vol 26 (11) ◽

pp. 2427-2447

Author(s):

S.N. Yashin ◽

E.V. Koshelev ◽

S.A. Borisov

Keyword(s):

Ant Colony Optimization ◽

Optimization Algorithm ◽

Population Decline ◽

Innovation System ◽

Federal District ◽

Original Model ◽

Ant Colony ◽

Industrial Agglomeration ◽

Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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