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Enhanced leukemia prediction using hybrid ant colony and ant lion optimization for gene selection and classification

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METHODSX 2025年 14卷 103239页

作者： Santhakumar, D. Rajaram, Gnanajeyaraman Elankavi, R. Viswanath, J. Govindharaj, I Raja, J. Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai 602105 Tamil Nadu India Vinayaka Missions Res Fdn Aarupadai Veedu Inst Technol Dept Comp Sci & Engn Chennai 603104 Tamil Nadu India Madanapalle Inst Technol & Sci Dept Comp Sci & Engn Artificial Intelligence Madanapalle 517325 Andhra Pradesh India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Chennai 600062 Tamil Nadu India

Gene selection plays a crucial role in the pre-processing of microarray data, aiming to identify a small set of genes that enhances classification accuracy and reduces costs. Traditional methods, such as Genetic Algorithms (GA) and Maximum Relevance Minimum Redundancy (MRMR), have been widely used, but bio-inspired algorithms like Ant Colony Optimization (ACO) and Ant Lion Optimizer (ALO) have shown promising results. These algorithms are based on natural processes: ACO mimics the foraging behavior of ants, while ALO models the hunting strategy of ant-lion larvae. However, both approaches face challenges like premature convergence and inefficient feature space mapping when used individually. To address these issues, this work introduces a hybrid ACO-ALO method, combining the strengths of both algorithms. The proposed hybrid approach enhances feature selection by improving accuracy, reducing computational complexity, and boosting classifier performance. The proposed model, which identifies the optimal feature set for classification using Support Vector Machine (SVM), has achieved an impressive prediction accuracy of 93.94 %. Results on microarray datasets for leukemia prediction show that the hybrid approach outperforms other methods in terms of both effectiveness and efficiency. This work demonstrates the potential of hybrid optimization techniques in bioinformatics for better gene selection and cancer diagnosis. center dot Hybrid ACO-ALO approach combines strengths of both algorithms for better feature selection. center dot Enhances classifier performance while reducing computational complexity. center dot Outperforms traditional methods on leukemia prediction datasets.

关键词： Ant Colony Optimization Ant Lion Optimizer Genetic algorithm Gene selection Leukemia prediction Maximum relevance minimum redundancy algorithm microarray data

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Feature selection and computational optimization in high-dimensional microarray cancer datasets via InfoGain-modified bat algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第25期81卷 36505-36549页

作者： Hambali, Moshood A. Oladele, Tinuke O. Adewole, Kayode S. Sangaiah, Arun Kumar Gao, Wei Fed Univ Wukari Dept Comp Sci Wukari Taraba State Nigeria Univ Ilorin Dept Comp Sci Ilorin Kwara State Nigeria Natl Yunlin Univ Sci & Technol Touliu 633102 Yunlin Taiwan Yunnan Normal Univ Sch Informat Sci & Technol Kunming Yunnan Peoples R China

Achieving a satisfactory cancer classification accuracy with the complete set of genes remains a great challenge, due to the high dimensions, small sample size, and presence of noise in gene expression data. Feature reduction is critical and sensitive in the classification task, most importantly in heterogeneous multimedia data. One of the major drawbacks in cancer study is recognizing informative genes from thousands of available genes in microarray data. Traditional feature selection algorithms have failed to scale on large space data like microarray data. Therefore, an effective feature selection algorithm is required to explore the most significant subset of genes by removing non-predictive genes from the dataset without compromising the accuracy of the classification algorithm. The study proposed an information Gain - Modified Bat Algorithm (InfoGain-MBA) features selection model for selecting relevant and informative features from high dimensional microarray cancer datasets and evaluate the approach with four classifiers - C4.5, Decision Tree, Random Forest and classification and regression tree (CART). The results obtained show that the proposed approach is promising for the classification of microarray cancer data. The random forest has 100% accuracy with few genes in all seven datasets used. Further investigations were also conducted to determine the optimal threshold for each of the datasets.

关键词： Feature selection Binary bat algorithm Information gain Cancer classification microarray data Random forest Computational optimization

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The Overexpression of CD80 and ISG15 Are Associated with the Progression and Metastasis of Breast Cancer by a Meta-Analysis Integrating Three microarray datasets

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PATHOLOGY & ONCOLOGY RESEARCH 2020年第1期26卷 443-452页

作者： Li, Yuanhang Bai, Weijun Zhang, Linlin China Med Univ Med Dept Canc Hosp 44 Xiaoheyan Rd Shenyang 110042 Liaoning Peoples R China

Breast cancer is a common cancer and could result in a substantial mortality. The study aimed to screen gene signatures associated with the development and metastasis of breast cancer and explore their regulation mechanisms. Three datasets of GSE10797, GSE8977 and GSE3744 were downloaded from GEO (Gene Expression Omnibus) database, containing 55 breast cancer samples and 27 normal samples. After data preprocessing using limma software and RMA (robust multi-array average) algorithm, DEGs (differentially expressed genes) between breast tumor and normal tissues in three individual experiments were identified using MADAM package. Function and pathway enrichment analyses were performed for the DEGs. Transcription factors and TAGs (tumor associated genes) among the DEGs were recognized and the PPI (protein-protein-interaction) network for the DEGs was constructed using Cytoscape software. The mRNA expression was analyzed via real-time quantitative PCR and protein expression was measured by western blotting. Totally, 100 DEGs were identified, including 33 up-regulated genes and 67 down-regulated genes. Among them, up-regulated DEGs such as CD80 was enriched in toll-like receptor (TLR) interaction pathway and the TAG, ISG15 was related to RIG-I-like receptor signaling pathway, while CXCL10 was involved in both of the two pathways. Whereas, the down-regulated DEG, CXCL12 was significantly associated with axon guidance pathway. Additionally, these DEGs were also pivotal nodes in the PPI network with high degrees. Besides, CXCL10 and CD80 were both interacted with IFNG. The mRNA expression of ISG15 was obviously enhanced in human breast cancer cells MCF-7, while no significant difference of CXCL10 mRNA level was found between MCF10A and MCF-7 cells. Moreover, the proteins expression levels of CD80 and ISG15 were significantly increased in MCF-7, MDA-MB-468 and MDA-MB-231 breast cancer cells than in normal MCF10A cells. CD80 might be responsible for the breast cancer's progress

关键词： microarray data Meta-analysis Breast cancer Metastasis

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Detection of colon cancer based on microarray dataset using machine learning as a feature selection and classification techniques

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SN APPLIED SCIENCES 2020年第7期2卷 1243页

作者： Shafi, A. S. M. Molla, M. M. Imran Jui, Julakha Jahan Rahman, Mohammad Motiur Mawlana Bhashani Sci & Technol Univ Dept Comp Sci & Engn Tangail 1902 Bangladesh Khwaja Yunus Ali Univ Fac Comp Sci & Engn Sirajgonj 6751 Bangladesh Univ Malaysia Pahang Fac Elect & Elect Engn Pekan 26600 Pahang Malaysia

microarray data is an increasingly important tool for providing information on gene expression for analysis and interpretation. Researchers attempt to utilize the smallest possible set of relevant gene expression profiles in most gene expression studies to enhance tumor identification accuracy. This research aims to analyze and predicts colon cancer data employing a machine learning approach and feature selection technique based on a random forest classifier. More particularly, our proposed method can reduce the burden of high dimensional data and allow faster calculations by combining the "Mean Decrease Accuracy" and "Mean Decrease Gini" as feature selection methods into a renowned classifier namely Random Forest, with the aim of increasing the prediction model's accuracy level. In addition, we have also shown a comparative model analysis with selection of features and model without selection of features. The extensive experimental results have demonstrated that the proposed model with feature selection is favorable and effective which triumphs the best performance of accuracy.

关键词： Colon cancer microarray data Feature selection Machine learning Random forest Cross validation

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Interval modelling in optimization of k- N N classifiers for large number of attributes in data sets on an example of DNA microarrays

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第6期37卷 3334-3372页

作者： Bentkowska, Urszula Bazan, Jan G. Zareba, Lech Socha, Jerzy Bazan-Socha, Stanislawa Mrukowicz, Marcin Univ Rzeszow Coll Nat Sci Inst Comp Sci PL-35310 Rzeszow Poland Small Mt Med Ctr Data Anal Unit Krakow Poland Jagiellonian Univ Med Coll Dept Internal Med Krakow Poland

In this contribution there are considered interval-valued methods of improving the quality of classification by the k- N N binary classifiers in the case of large number of attributes in data sets. One of the possible applications of the introduced method are the microarray data so the presented results may be applied in medical diagnosis support, for example, in identification of marker genes. The proposed algorithm involving reduction methods of time complexity and interval modeling of data using interval-valued aggregation functions has a significantly higher classification quality than the other considered algorithm with the aggregation method involving the numerical arithmetic mean. The former algorithm is based on the interval-valued aggregation of uncertainty intervals determined on the basis of certainty coefficients of individual k- N N classifiers while the latter is based on the aggregation of certainty coefficients of individual k- N N classifiers with the use of the numerical arithmetic mean. Moreover, the performance of the new algorithm based on interval-valued methods was compared with the known from the literature classifiers in cancer diagnosis based on gene expression microarrays. The obtained results prove that in practical applications the newly proposed algorithm can be successfully used.

关键词： aggregation function classifier interval-valued fuzzy set k-NN classifier microarray data

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Deep learning-based microarray cancer classification and ensemble gene selection approach

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IET SYSTEMS BIOLOGY 2022年第3-4期16卷 120-131页

作者： Rezaee, Khosro Jeon, Gwanggil Khosravi, Mohammad R. Attar, Hani H. Sabzevari, Alireza Meybod Univ Dept Biomed Engn Meybod Iran Incheon Natl Univ Coll Informat Technol Dept Embedded Syst Engn Incheon South Korea Persian Gulf Univ Dept Comp Engn Bushehr Iran Zarqa Univ Dept Energy Engn Zarqa Jordan

Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k-nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis-related brain tissue lesions were examined to show the generalisability of the model method.

关键词： soft ensembling lab-on-a-chip ensemble gene selection approach feature selection medical computing deep learning (artificial intelligence) k-nearest neighbour algorithm diseases genetic origins microarray data diffuse large cell lymphoma deep learning-based microarray cancer classification gene expression stacked deep neural network wrapper genes data analysis feature selection approach microarray datasets cancer pattern classification genetics effective genes prostate cancer brain tumours low error levels combination strategy

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Identifying the candidate genes using co-expression, GO, and machine learning techniques for Alzheimer's disease

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2022年第1期11卷 1-12页

作者： Sahu, Shailendra Dholaniya, Pankaj Singh Rani, T. Sobha Univ Hyderabad Sch Comp & Informat Sci Hyderabad India Univ Hyderabad Dept Biotechnol & Bioinformat Hyderabad India

Alzheimer's disease is a neurological disorder that affects an individual's memory, motor functions, behaviour, and thought process. It has been observed that the hippocampus is the first region that gets affected by Alzheimer's. Hence, a study of the hippocampus region can identify genes responsible for the occurrence of the early stage of the disease. Most often, t-test and correlation are used to identify significant genes at the initial level. As the genes are differentially expressed, their classification power is generally high. These genes might appear significant, but their degree of specificity towards the disease might be low, leading to misleading interpretations. Similarly, there may be many false correlations between the genes that can affect the identification of relevant genes. This paper introduces a new framework to reduce the false correlations and find the potential biomarkers for the disease. The framework concerned uses the t-test, correlation, Gene Ontology (GO) categories, and machine learning techniques to find potential genes. The proposed framework detects Alzheimer-related genes and achieves more than 95% classification accuracy in every dataset considered. Some of the identified genes which are directly involved in Alzheimer are APP, GRIN2B, and APLP2. The proposed framework also identifies genes like ZNF621, RTF1, DCH1, and ERBB4, which may play an important role in Alzheimer's. Gene set enrichment analysis (GSEA) is also carried out to determine the major GO categories: down-regulated and up-regulated.

关键词： microarray data Gene co-expression network Gene ontology similarity Feature selection Classification

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Predictive Analysis of microarray data

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Open Journal of Genetics 2014年第1期4卷 63-68页

作者： Paulo C.Marques F. Carlos A.de B.Pereira Departamento de Estatistica Instituto de Matematica e EstatísticaUniversidade de Sao PauloSao PauloBrasil

microarray gene expression data are analyzed by means of a Bayesian nonparametric model, with emphasis on prediction of future observables, yielding a method for selection of differentially expressed genes and the cor... 详细信息

关键词： Bayesian Nonparametrics Dirichlet Process microarray data Differential Gene Expression Classification

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Identifying Cancer Biomarkers From microarray data Using Feature Selection and Semisupervised Learning

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IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2014年 2卷 4300211-4300211页

作者： Chakraborty, Debasis Maulik, Ujjwal Murshidabad Coll Engn & Technol Berhampur 742102 India Jadavpur Univ Kolkata 700032 India

microarrays have now gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to novel algorithms for analyzing changes in expression profiles. In a micro-RNA (miRNA) or gene-expression profiling experiment, the expression levels of thousands of genes/miRNAs are simultaneously monitored to study the effects of certain treatments, diseases, and developmental stages on their expressions. microarray-based gene expression profiling can be used to identify genes, whose expressions are changed in response to pathogens or other organisms by comparing gene expression in infected to that in uninfected cells or tissues. Recent studies have revealed that patterns of altered microarray expression profiles in cancer can serve as molecular biomarkers for tumor diagnosis, prognosis of diseases-pecific outcomes, and prediction of therapeutic responses. microarray data sets containing expression profiles of a number of miRNAs or genes are used to identify biomarkers, which have dysregulation in normal and malignant tissues. However, small sample size remains a bottleneck to design successful classification methods. On the other hand, adequate number of microarray data that do not have clinical knowledge can be employed as additional source of information. In this paper, a combination of kernelized fuzzy rough set (KFRS) and semisupervised support vector machine (S3VM) is proposed for predicting cancer biomarkers from one miRNA and three gene expression data sets. Biomarkers are discovered employing three feature selection methods, including KFRS. The effectiveness of the proposed KFRS and S3VM combination on the microarray data sets is demonstrated, and the cancer biomarkers identified from miRNA data are reported. Furthermore, biological significance tests are conducted for miRNA cancer biomarkers.

关键词： Cancer biomarkers feature selection kernelized fuzzy rough set microarray data semisupervised SVM successive filtering.

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Model-free Bias Reduction of Storey’s Estimator for the Proportion of True Null Hypotheses

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Calcutta Statistical Association Bulletin 2022年第1期74卷 27-41页

作者： Biswas, Aniket Department of Statistics Dibrugarh University Assam Dibrugarh India

Storey’s bootstrap estimator for the proportion of true null hypotheses (π0) has a known bias structure but the amount of bias is obviously unknown. A new method has been proposed in this work to reduce bias of the ... 详细信息

Storey’s bootstrap estimator for the proportion of true null hypotheses (π₀) has a known bias structure but the amount of bias is obviously unknown. A new method has been proposed in this work to reduce bias of the estimator. The proposed method partitions differential gene expression and similar data sets randomly and repeatedly to first approximate the bias and then corrects it in the final estimate. Unlike some recent developments, the method does not require any model assumption. The proposed method depends on some tuning parameters. For practical applicability of the method, ideal choices of the tuning parameters are given based on numerical results. Extensive simulation experiments show favourable results for the proposed method over the popular robust estimators in the literature. Two real-life microarray data sets have been studied to demonstrate the applicability of the final bias and variance reduced estimator. AMS 2010 subject classifications: 62F10, 62F40 © 2022 Calcutta Statistical Association, Kolkata.

关键词： microarray data p-value Repeated random partitioning simultaneous testing t -test upper tail probability

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