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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2023年第2期22卷 383-392页

作者： Dey, Anirban Das Sharma, Kaushik Sanyal, Tamalika Bhattacharjee, Pritha Bhattacharjee, Pritha Univ Calcutta Dept Appl Phys Kolkata 700009 India Univ Calcutta Dept Environm Sci Kolkata 700019 India

Arsenic is a carcinogen, and long-term exposure to it may result in the development of multi-organ disease. Understanding the underlying intricate molecular network of toxicity and carcinogenicity is crucial for identifying a small set of differentially expressed biomarker genes to predict the risk of the exposed population. In this paper, a multiple kernel learning (MKL) embedded multi-objective swarm intelligence technique has been proposed to identify the candidate biomarker genes from the transcriptomic profile of arsenicosis samples. To achieve the optimal classification accuracy along with the minimum number of genes, a multi-objective random spatial local best particle swarm optimization (MO-RSplbestPSO) has been utilized. The proposed MO-RSplbestPSO also guides the multiple kernel learning mechanism which provides data specific classification. The proposed computational framework has been applied to the developed whole genome DNA microarray prepared using blood samples collected from a specific arsenic exposed area of the Indian state of West Bengal. A set of twelve biomarker genes, with four novel genes, are successfully identified for the classification of exposure to arsenic and its subcategories, which can be used as future prognostic biomarkers for screening of arsenic exposed populations. Also, the biological significance of each gene is detailed to delineate the complex molecular networking and mode of toxicity.

关键词： Biomarkers Arsenic Particle swarm optimization Biology Optimization Kernel Sociology Multi objective particle swarm optimization gene selection microarray data biomarker arsenicosis

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Genetic Algorithms and Feature Selection for Improving the Classification Performance in Healthcare

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第3期15卷 737-744页

作者： Alassaf, Alaa Alarbeed, Eman Alrasheed, Ghady Almirdasie, Abdulsalam Almutairi, Shahd Al-Hagery, Mohammed Abullah Saeed, Faisal Qassim Univ Coll Comp Dept Comp Sci Qasim Saudi Arabia Birmingham City Univ Coll Comp & Digital Technol Birmingham W Midlands England Qassim Univ Deanship Sci Res Qasim Saudi Arabia

microarray technology appeared recently and is used in genetic research to study gene expressions. microarray has been widely applied to many fields, especially the health sector, such as diagnosing and predicting diseases, specifically cancer diseases. These experiments usually generate a huge amount of gene expression data with analytical and computational complexities. Therefore, feature selection techniques and different classifications help solve these problems by eliminating irrelevant and redundant features. This paper presents a proposed method for classifying the data using eight classifications machine learning algorithms. Then, the Genetic Algorithm (GA) is applied to improve the selection of the best features and parameters for the model. We use the higher accuracy of the model among the different classifications as a measure of fit in the genetic algorithm;this means that the model's accuracy can be used to select the best solutions than others in the community. The proposed method was applied to the colon, breast, prostate, and Central Nervous System (CNS) diseases and experimental outcomes demonstrated an accuracy rate of 93.75, 96.15, 82.76, and 93.33 respectively. Based on these findings, the proposed method works well and effectively.

关键词： Cancer classification gene expression feature selection microarray data algorithm machine learning genetic algorithm

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A two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization

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BMC BIOINFORMATICS 2023年第1期24卷 1-27页

作者： Li, Wei Chi, Yuhuan Yu, Kun Xie, Weidong Northeastern Univ Key Lab Intelligent Comp Med Image MIIC Minist Educ Shenyang Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Sch Biomed & Informat Engn Shenyang Peoples R China

BackgroundIn the field of genomics and personalized medicine, it is a key issue to find biomarkers directly related to the diagnosis of specific diseases from high-throughput gene microarray data. Feature selection technology can discover biomarkers with disease classification *** use support vector machines as classifiers and use the five-fold cross-validation average classification accuracy, recall, precision and F1 score as evaluation metrics to evaluate the identified biomarkers. Experimental results show classification accuracy above 0.93, recall above 0.92, precision above 0.91, and F1 score above 0.94 on eight microarray *** paper proposes a two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization (EF-BDBA), which can effectively reduce the dimension of microarray data and obtain optimal biomarkers. In the first stage, we propose an ensemble filter feature selection method. The method combines an improved fast correlation-based filter algorithm with Fisher score. obviously redundant and irrelevant features can be filtered out to initially reduce the dimensionality of the microarray data. In the second stage, the optimal feature subset is selected using an improved binary differential evolution incorporating an improved binary African vultures optimization algorithm. The African vultures optimization algorithm has excellent global optimization ability. It has not been systematically applied to feature selection problems, especially for gene microarray data. We combine it with a differential evolution algorithm to improve population *** with traditional feature selection methods and advanced hybrid methods, the proposed method achieves higher classification accuracy and identifies excellent biomarkers while retaining fewer features. The experimental results demonstrate the effectiveness and advancement of ou

关键词： Feature selection Ensemble filter method Binary differential evolution African vultures optimization microarray data

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Level Sets Semimetrics for Probability Measures with Applications in Hypothesis Testing

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METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 2023年第1期25卷 1-17页

作者： Munoz, Alberto Martos, Gabriel Gonzalez, Javier Univ Carlos III Madrid Calle Madrid 126 Getafe 28903 Madrid Spain Univ Torcuato Tella Av Figueroa Alcorta RA-7350 Buenos Aires Argentina Microsoft Res Cambridge 21 Stn Rd Cambridge CB1 2FB England

In this paper we introduce a novel family of level sets semimetrics for density functions and address subtleties entailed in the estimation and computation of such semimetrics. Given data drawn from f and q, two unknown density functions, we consider different level set semimetrics so to test the null hypothesis H0 : f = q . The performance of such testing procedure is show-cased in a Monte Carlo simulation study. Using the methods developed in the paper, we assess differences in gene expression profiles between two groups of patients with different respiratory recovery patterns in a clinical study;and find significant differences between the 15 top-ranked genes density profiles corresponding to the two groups.

关键词： Level sets semimetrics Density estimation Hypothesis testing Permutation test microarray data

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The exploration of shared genes and molecular mechanisms of systemic lupus erythematosus and atherosclerosis

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LUPUS 2023年第2期32卷 239-251页

作者： Fan, Ji-Lin Wu, Di Zhu, Ting-Ting Tian, Xiao-Ling Liu, Si-Jia Zhang, Shi-Liang Shandong Univ Tradit Chinese Med Clin Sch Med 1 Jinan Peoples R China Binzhou Med Univ Hosp Dept Neurosurg Binzhou Peoples R China Shandong Univ Tradit Chinese Med Affiliated Hosp Dept Cardiol 16369 Jingshi Rd Jinan 250014 Peoples R China

Objective Despite widespread recognition, the mechanisms underlying the relationship between systemic lupus erythematosus (SLE) and atherosclerosis (AS) are still unclear. Our study aimed to explore the shared genetic signature and molecular mechanisms of SLE and AS using a bioinformatics approach. Methods Gene expression profiles of GSE50772 (contains peripheral blood mononuclear cells from 61 SLE patients and 20 normal samples) and GSE100927 (contains 69 AS plaque tissue samples and 35 control samples) were downloaded from the Gene Expression database (GEO) before the differentially expressed genes were obtained using the "limma" package in R. The differential genes were then subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the DAVID online platform to annotate their functions. The intersection targets of PPI and WGCNA were used as key shared genes for SLE and AS with their diagnostic value as shared genes being verified through ROC curves. Finally, Cytoscape 3.7.2 software was used to construct a miRNA-mRNA network map associated with the shared genes. Results A total of 246 DEGs were identified, including 189 upregulated genes and 57 downregulated genes, which were mainly enriched in signaling pathways such as TNF signaling pathway, IL-17 signaling pathway, and NF-kB signaling pathway. The molecular basis for the relationship between SLE and AS may be the aforementioned signaling pathways. Following ROC curve validation, the intersection of PPI and WGCNA, as well as AQP9, CCR1, CD83, CXCL1, and FCGR2A, resulted in the identification of 15 shared genes. Conclusion The study provided a new perspective on the common molecular mechanisms between SLE and AS, and the key genes and pathways that were identified as being part of these pathways may offer fresh perspectives and suggestions for further experimental research.

关键词： Systemic lupus erythematosus atherosclerosis microarray data molecular mechanisms

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Symmetric uncertainty class-feature association map for feature selection in microarray dataset

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第1期11卷 15-32页

作者： Bakhshandeh, Soodeh Azmi, Reza Teshnehlab, Mohammad Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Alzahra Univ Dept Comp Engn Tehran Iran KN Toosi Univ Technol Dept Control Engn Tehran Iran

For a huge number of features versus a small size of samples, feature selection methods are useful preprocessing approaches that could eliminate the irrelevant and redundant features from the final feature subset. One of the recent research areas in feature selection is DNA microarray that the number of dimensions increase fast and requires further research in the field of feature selection. Modeling the feature search space as a graph leads to improving the visualizing of features and using graph theoretic concepts in the feature selection process. In this paper, a filer-based feature selection algorithm using graph technique is proposed for reducing the dimension of dataset named as Symmetric Uncertainty Class-Feature Association Map feature selection (SU-CFAM). In the first step, it uses the Symmetric Uncertainty concept for visualizing the feature search space as a graph. After clustering the graph into several clusters using a community detection algorithm, SU-CFAM constructs an adjacency matrix for each cluster and the final subset is selected by using the concept of maximal independent set. The performance of SU-CFAM has been compared with five well-known feature selection approaches using three classifiers including SVM, DT, NB. Experiments on fifteen public DNA microarray datasets show that SU-CFAM can achieve a better classification performance compared with other methods.

关键词： Feature selection microarray data Graph-theoretic approach Class-Feature Association Map

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AltWOA: Altruistic Whale Optimization Algorithm for feature selection on microarray datasets

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 144卷 105349-105349页

作者： Kundu, Rohit Chattopadhyay, Soham Cuevas, Erik Sarkar, Ram Jadavpur Univ Dept Elect Engn Kolkata 700032 India Univ Guadalajara Dept Elect CUCEI Av Revolut 1500 Guadalajara Jal Mexico Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India

The data-driven modern era has enabled the collection of large amounts of biomedical and clinical data. DNA microarray gene expression datasets have mainly gained significant attention to the research community owing to their ability to identify diseases through the "bio-markers" or specific alterations in the gene sequence that represent that particular disease (for example, different types of cancer). However, gene expression datasets are very high-dimensional, while only a few of those are "bio-markers". Meta-heuristic-based feature selection effectively filters out only the relevant genes from a large set of attributes efficiently to reduce data storage and computation requirements. To this end, in this paper, we propose an Altruistic Whale Optimization Algorithm (AltWOA) for the feature selection problem in high-dimensional microarray data. AltWOA is an improvement on the basic Whale Optimization Algorithm. We embed the concept of altruism in the whale population to help efficient propagation of candidate solutions that can reach the global optima over the iterations. Evaluation of the proposed method on eight high dimensional microarray datasets reveals the superiority of AltWOA compared to popular and classical techniques in the literature on the same datasets both in terms of accuracy and the final number of features selected. The relevant codes for the proposed approach are available publicly at https://gith ***/Rohit-Kundu/AltWOA.

关键词： Feature selection Evolutionary meta-heuristic Altruism Cancer detection Gene expression microarray data

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Screening and identification of the core immune-related genes and immune cell infiltration in severe burns and sepsis

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JOURNAL OF CELLULAR AND MOLECULAR MEDICINE 2023年第11期27卷 1493-1508页

作者： Su, Wenxing Li, Wei Zhang, Yuanyuan Wang, Kuan Chen, Maolin Chen, Xiaoming Li, Dazhuang Zhang, Ping Yu, Daojiang China Natl Nucl Corp 416 Hosp Chengdu Med Coll Affiliated Hosp 2 Dept Plast & Burns Surg Chengdu Peoples R China First Affiliated Hosp Chengdu Med Coll Dept Urol Chengdu Peoples R China Xindu Dist Peoples Hosp Chengdu Dept Med Lab Chengdu Peoples R China First Affiliated Hosp Chengdu Med Coll Dept Cosmet Plast & Burns Surg Chengdu Peoples R China Yangzhou Univ Affiliated Hosp Dept Orthoped Yangzhou Peoples R China China Natl Nucl Corp 416 Hosp Chengdu Med Coll Affiliated Hosp 2 Dept Plast & Burns Surg 4 Er Huan Lu Bei Si Duan Chengdu 610051 Sichuan Peoples R China First Affiliated Hosp Chengdu Med Coll Dept Cosmet Plast & Burns Surg Chengdu Sichuan Peoples R China 278 Middle Baoguang Rd Chengdu 610599 Sichuan Peoples R China Yangzhou Univ Affiliated Hosp Dept Orthoped 45 Taizhou Rd Yangzhou 225000 Jiangsu Peoples R China

Severe burns often have a high mortality rate due to sepsis, but the genetic and immune crosstalk between them remains unclear. In the present study, the GSE77791 and GSE95233 datasets were analysed to identify immune-related differentially expressed genes (DEGs) involved in disease progression in both burns and sepsis. Subsequently, weighted gene coexpression network analysis (WGCNA), gene enrichment analysis, protein-protein interaction (PPI) network construction, immune cell infiltration analysis, core gene identification, coexpression network analysis and clinical correlation analysis were performed. A total of 282 common DEGs associated with burns and sepsis were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the following enriched pathways in burns and sepsis: metabolic pathways;complement and coagulation cascades;legionellosis;starch and sucrose metabolism;and ferroptosis. Finally, six core DEGs were identified, namely, IL10, RETN, THBS1, FGF13, LCN2 and MMP9. Correlation analysis showed that some core DEGs were significantly associated with simultaneous dysregulation of immune cells. Of these, RETN upregulation was associated with a worse prognosis. The immune-related genes and dysregulated immune cells in severe burns and sepsis provide potential research directions for diagnosis and treatment.

关键词： burns core immune-related genes differentially expressed genes immune cell infiltration microarray data sepsis

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Optimizing microarray Gene Selection in Colon Cancer: An Enhanced Metaheuristic Algorithm for Feature Selection 2nd

Optimizing Microarray Gene Selection in Colon Cancer: An Enh...

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2nd International Conference on Information Technologies and Their Applications

作者： Benghazouani, Salsabila Nouh, Said Zakrani, Abdelali Hassan II Univ Fac Sci Ben MSik Dept Math & Comp Sci Casablanca Morocco ENSAM II Univ Dept Comp Sci Engn Casablanca Morocco

ISBN: (纸本)9783031734199;9783031734205

microarray data has evolved into an indispensable tool for scrutinizing gene expression, prompting researchers to strategically utilize a minimal set of pertinent gene expression profiles to refine the accuracy of tumor identification. The imperative task of identifying key differential genes in colon cancers, crucial for distinguishing patients from the normal population, has led to the development of various techniques and algorithms. Swarm and Evolutionary Algorithms (SEA), renowned for their efficiency as global search agents, have emerged as particularly effective in optimizing the selection of a pertinent subset of genes related to colon cancer. This paper introduces an innovative approach that integrates swarm and evolutionary algorithms to tackle the challenge of feature selection in datasets related to colon cancer. A thorough comparative analysis is conducted, high-lighting the differences between the proposed method and an alternative feature selection approach based on swarm and evolutionary algorithms. The extensive experimental results convincingly demonstrate the favorability and effectiveness of the proposed model, showcasing superior accuracy and a notable reduction in the number of selected features.

关键词： Feature selection Swarm and Evolutionary Algorithms microarray data Colon cancer Classification Machine learning

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Differential Expression of MicroRNAs and Predicted Drug Target in Amyotrophic Lateral Sclerosis

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JOURNAL OF MOLECULAR NEUROSCIENCE 2023年第6期73卷 375-390页

作者： Patel, Riya Ben Bajpai, Akhilesh Kumar Thirumurugan, Kavitha Vellore Inst Technol Sch Biosci & Technol Struct Biol Lab 412JPearl Res Pk Vellore 632014 India Univ Tennessee Hlth Sci Ctr Dept Genet Genom & Informat Memphis TN 38163 USA

ALS (Amyotrophic Lateral Sclerosis) is a rare type of neurodegenerative disease. It shows progressive degradation of motor neurons in the brain and spinal cord. At present, there is no treatment available that can completely cure ALS. The available treatments can only increase a patient's life span by a few months. Recently, microRNAs (miRNAs), a sub-class of small non-coding RNAs have been shown to play an essential role in the diagnosis, prognosis, and therapy of ALS. Our study focuses on analyzing differential miRNA profiles and predicting drug targets in ALS using bioinformatics and computational approach. The study identifies eight highly differentially expressed miRNAs in ALS patients, four of which are novel. We identified 42 hub genes for these eight highly expressed miRNAs with Amyloid Precursor Protein (APP) as a candidate gene among them for highly expressed down-regulated miRNA, hsa-miR-455-3p using protein-protein interaction network and Cytoscape analysis. A novel association has been found between hsa-miR-455-3p/APP/serotonergic pathway using KEGG pathway analysis. Also, molecular docking studies have revealed curcumin as a potential drug target that may be used for the treatment of ALS. Thus, the present study has identified four novel miRNA biomarkers: hsa-miR-3613-5p, hsa-miR-24, hsa-miR-3064-5p, and hsa-miR-4455. There is a formation of a novel axis, hsa-miR-455-3p/APP/serotonergic pathway, and curcumin is predicted as a potential drug target for ALS.

关键词： Amyotrophic lateral sclerosis MicroRNAs microarray data Target genes Drug targets

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