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Identifying candidate biomarkers for detecting bronchogenic carcinoma stages using metaheuristic algorithms based on information fusion theory

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Discover Oncology 2025年第1期16卷 1-27页

作者： Khalvati, Bagher Kavousi, Kaveh Keyhanipour, Amir Hosein Arabfard, Masoud Department of Bioinformatics Kish International Campus University of Tehran Kish Iran Laboratory of Complex Biological Systems and Bioinformatics (CBB) Department of Bioinformatics Institute of Biochemistry and Biophysics (IBB) University of Tehran Tehran Iran Computer Engineering Department Faculty of Engineering College of Farabi University of Tehran Tehran Iran Chemical Injuries Research Center Systems Biology and Poisonings Institute Baqiyatallah University of Medical Sciences Tehran Iran

Objective: Invasive lung cancer staging poses significant challenges, often requiring painful and costly biopsy procedures. This study aims to identify non-invasive biomarkers for detecting bronchogenic carcinoma and its various stages by analyzing gene expression data using bioinformatics and machine learning techniques. By leveraging these advanced computational methods, we seek to eliminate the need for surgical intervention in the diagnostic process. Methods: We utilized the TCGA-LUAD dataset, including gene expression data from healthy and cancerous samples. To identify robust biomarkers, we applied eight metaheuristic algorithms for feature selection, combined with four classification methods and two data fusion techniques to optimize performance. Results: Our approach achieved 100% accuracy in distinguishing healthy samples from cancerous ones, outperforming existing methods that reported 97% accuracy. Notably, while prior methods have struggled to separate bronchogenic carcinoma stages effectively, our research achieved an approximate accuracy of 77% in stage classification. Furthermore, using gene enrichment methods, we identified 5, 7, and 16 diagnostic biomarker candidates for stages I, II, III, and IV, respectively. Conclusion: This study demonstrates that integrating bioinformatics, gene set enrichment, and biological pathway analysis can enable non-invasive diagnostics for bronchogenic carcinoma stages. These findings hold promise for developing alternatives to traditional, invasive staging systems, potentially improving patient outcomes and reducing healthcare costs. © The Author(s) 2025.

关键词： Biomarker Bronchogenic carcinoma feature selection algorithms Information fusion Machine learning

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Implementing attack detection system using filter-based feature selection methods for fog-enabled IoT networks

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TELECOMMUNICATION SYSTEMS 2022年第1期81卷 23-39页

作者： Chaudhary, Pooja Gupta, Brij Singh, A. K. Natl Inst Technol Dept Comp Engn Kurukshetra Haryana India Asia Univ Int Ctr AI & Cyber Secur Res & Innovat Taichung 413 Taiwan Asia Univ Dept Comp Sci & Informat Engn Taichung 413 Taiwan Skyline Univ Coll Res & Innovat Dept POB 1797 Sharjah U Arab Emirates King Abdulaziz Univ Dept Comp Sci Jeddah 21589 Saudi Arabia

Internet-of-Things (IoT) has become an enthralling attacking surface for attackers to explode multitude of cyber-attacks. Distributed Denial of Service (DDoS) attack has transpired as the most menacing attack in the IoT networks. In this article, we propose an attack detection system to identify anomalous activities in the fog-enabled IoT network. Initially, authors have investigated exhaustively on the performance of filter-based feature selection algorithms comprising ReliefF, Correlation feature selection (CFS), Information Gain (IG), and Minimum-Redundancy-Maximum-Relevancy (mRMR) and distinct categories classification algorithms upon the prepared dataset consisting of IoT network specific features. Performance of the tested classification algorithm is assessed using prominent evaluation measures. Moreover, response time of classifiers is calculated for centralized and fog-enabled IoT network infrastructure. The experimental outcomes unveil that, in terms of both accuracy and latency, J48 classifier outperforms all other tested classifier with mRMR feature selection algorithm.

关键词： Internet of things (IoT) security feature selection algorithms DDoS attack Machine learning classifiers Intrusion detection system (IDS)

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A Hybrid Approach for feature selection Based on Genetic Algorithm and Recursive feature Elimination

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INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN 2021年第2期12卷 17-38页

作者： Rani, Pooja Kumar, Rajneesh Jain, Anurag Chawla, Sunil Kumar Maharishi Markandeshwar Deemed Maharishi Markandeshwar Engn Coll MM Inst Comp Technol & Business Management Mullana India Maharishi Markandeshwar Deemed Dept CSE Maharishi Markandeshwar Engn Coll Mullana India Univ Petr & Energy Studies Sch Comp Sci Virtualizat Dept Dehra Dun Uttarakhand India Chandigarh Univ Univ Inst Engn Comp Sci & Engn Chandigarh Punjab India

Machine learning has become an integral part of our life in today's world. Machine learning when applied to real-world applications suffers from the problem of high dimensional data. Data can have unnecessary and redundant features. These unnecessary features affect the performance of classification systems used in prediction. selection of important features is the first step in developing any decision support system. In this paper, the authors have proposed a hybrid feature selection method GARFE by integrating GA (genetic algorithm) and RFE (recursive feature elimination) algorithms. Efficiency of proposed method is analyzed using support vector machine classifier on the scale of accuracy, sensitivity, specificity, precision, F-measure, and execution time parameters. Proposed GARFE method is also compared to eight other feature selection methods. Results demonstrate that the proposed GARFE method has increased the performance of classification systems by removing irrelevant and redundant features.

关键词： Decision Support System feature selection algorithms Genetic Algorithm Machine Learning Recursive feature Elimination Support Vector Machine

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Supervised Low Rank Matrix Approximation for Stable feature selection

Supervised Low Rank Matrix Approximation for Stable Feature ...

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11th IEEE International Conference on Machine Learning and Applications (ICMLA)

作者： Alelyani, Salem Liu, Huan Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9780769549132;9781467346511

Increasing attention has been focused on the stability of selected features or selection stability, which is becoming a new measure in determining the effectiveness of a feature selection algorithm besides the learning performance. A recent study has shown that data characteristics play a significant role in selection stability. Hence, the solution to selection instability should begin with data. In this work, we propose a novel framework with a noise-reduction step before feature selection. Noise reduction is achieved via well-known low rank matrix approximation techniques (namely SVD and NMF) in a supervised manner to reduce data noise and variance between samples from the same class. The new framework is empirically shown to be highly effective with real high-dimensional datasets improving both selection stability and the precision of selecting relevant features while maintaining the classification accuracy for various feature selection methods.

关键词： feature selection algorithms stability Noise Reduction Low Rank Approximation

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Improved feature selection Based On Normalized Mutual Information 14

Improved Feature Selection Based On Normalized Mutual Inform...

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International Symposium Distributed Computing and Applications Business Engineering and Science

作者： Li Yin Ma Xingfei Yang Mengxi Zhao Wei Gu Wenqiang WuXi Vocat Inst Commerce Educ Informat Res Ctr Wuxi Peoples R China Shanghai Huaqin Telecom Co Ltd Software Div Dept 1 Shanghai Peoples R China

ISBN: (纸本)9781467365932

For the question (NMIFS) algorithm has the disadvantages of redundancy. This paper introduces a new feature selection method by enhanced NMIFS algorithm. A new quality estimation function is introduced in the new feature selection algorithm to overcome the shortcomings of the classic NMIFS, and the experiment shows on that normalized mutual information feature selection The experiment shows that the INMIFS can generate impressive results in accuracy and redundancy.

关键词： Mutual Information (MI) feature selection algorithms Normalized Mutual Information feature selection (NMIFS)

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An Ontological Approach for Recommending a feature selection Algorithm 22nd

An Ontological Approach for Recommending a Feature Selection...

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22nd International Conference on Web Engineering (ICWE)

作者： Nayak, Aparna Bozic, Bojan Longo, Luca Technol Univ Dublin SFI Ctr Res Training Machine Learning Sch Comp Sci Dublin Ireland

ISBN: (纸本)9783031099175;9783031099168

feature selection plays an important role in machine learning or data mining problems. Removing irrelevant features increases model accuracy and reduces the computational cost. However, selecting important features is not a simple task as one feature selection algorithm does not perform well on all the datasets that are of interest. This paper tries to address the recommendation of a feature selection algorithm based on dataset characteristics and quality. The research uses three types of dataset characteristics along with data quality metrics. The main contribution of the work is the utilization of Semantic Web techniques to develop a novel system that can aid in robust feature selection algorithm recommendations. The system's strength lies in assisting users of machine learning algorithms by providing more relevant feature selection algorithms for the dataset using an ontology called feature selection algorithm recommendation based on Data Characteristics and Quality (FSDCQ). Results are generated using six different feature selection algorithms and four types of classifiers on ten datasets from UCI repository. Recommendations take the form of "feature selection algorithm X is recommended for dataset i, as it performed better on dataset j, similar to dataset i in terms of class overlap 0.3, label noise 0.2, completeness 0.9, conciseness 0.8 units". While the domain-specific ontology FSDCQ was created to aid in the task of algorithm recommendation for feature selection, it is easily applicable to other meta-learning scenarios.

关键词： feature selection algorithms Meta-features Ontology

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feature selection and machine learning methods for optimal identification and prediction of subtypes in Parkinson's disease

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021年 206卷 106131-106131页

作者： Salmanpour, R. Mohammad Shamsaei, Mojtaba Rahmim, Arman Amirkabir Univ Technol Dept Energy Engn & Phys Tehran Iran Univ British Columbia Dept Phys & Astron Vancouver BC Canada Univ British Columbia Dept Radiol Vancouver BC Canada

Objectives: The present work focuses on assessment of Parkinson's disease (PD), including both PD subtype identification (unsupervised task) and prediction (supervised task). We specifically investigate optimal feature selection and machine learning algorithms for these tasks. Methods: We selected 885 PD subjects as derived from longitudinal datasets (years 0-4;Parkinson's Progressive Marker Initiative), and investigated 981 features including motor, non-motor, and imaging features (SPECT-based radiomics features extracted using our standardized SERA software). Two different hybrid machine learning systems (HMLS) were constructed and applied to the data in order to select optimal combinations in both tasks: (i) identification of subtypes in PD (unsupervised-clustering), and (ii) prediction of these subtypes in year 4 (supervised-classification). From the original data based on years 0 (baseline) and 1, we created new datasets as inputs to the prediction task: (i,ii) CSD0 and CSD01: cross-sectional datasets from year 0 only and both years 0 & 1, respectively;(iii) TD01: timeless dataset from both years 0 & 1. In addition, PD subtype in year 4 was considered as outcome. Finally, high score features were derived via ensemble voting based on their prioritizations from feature selector algorithms (FSAs). Results: In clustering task, the most optimal combinations (out of 981) were selected by individual FSAs to enable high correlation compared to using all features (arriving at 547). In prediction task, we were able to select optimal combinations, resulting in an accuracy >90% only for timeless dataset (TD01);there, we were able to select the most optimal combination using 77 features, directly selected by FSAs. In both tasks, however, using combination of only high score features from ensemble voting did not enable acceptable performances, showing optimal feature selection via individual FSAs to be more effective. Conclusion: Combining non-imaging information with SPECT-

关键词： Parkinson's disease Subtype identification Outcome prediction Radiomics feature selection algorithms Ensemble Voting

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Enhancing Prostate Cancer Classification by Leveraging Key Radiomics features and Using the Fine-Tuned Linear SVM Algorithm

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IEEE ACCESS 2023年 11卷 88025-88039页

作者： Varan, Metin Azimjonov, Jahongir Macal, Bilgen Sakarya Univ Appl Sci Dept Elect & Elect Engn Sakarya Turkiye Andijan State Univ Dept Informat Technol Andijan Uzbekistan Chungbuk Natl Univ Sch Informat & Commun Engn Cheongju 28644 South Korea Sakarya Univ Appl Sci Dept Elect & Comp Educ Sakarya Turkiye

This paper focuses on enhancing machine learning (ML)-based diagnosis and clinical decision-making by leveraging radiomics data, which provides a quantitative description of grayscale medical images such as MRI, CT, PET, or X-Ray. Extracted using advanced mathematical and statistical analysis methods, this data comprises hundreds of relevant and irrelevant radiomics features. The study underscores the critical importance of selecting the most relevant and efficient features to enhance ML-based diagnosis and clinical decision-making processes. To address this challenge, the paper introduces an accurate binary prostate cancer classification algorithm that integrates linear support vector machines (SVM) and ridge regression-based four-feature selection algorithms. The algorithm's performance was evaluated using the PROSTATEx dataset. Notably, when trained on feature subsets selected through importance coefficient, forward- and backward-sequential, and correlation coefficient-based feature selectors, the algorithm achieved classification accuracy exceeding 90%. However, when trained on the full set of features, the algorithm achieved 43.64% classification accuracy. These findings underscore the pivotal role of feature selection in achieving higher accuracy and speed during the training and testing of ML algorithms. Overall, the results indicate that the proposed algorithm can substantially improve the accuracy of prostate cancer classification. Furthermore, the findings have broader implications for the development of more efficient ML-based diagnosis and clinical decision-making systems in the field of gray-scale medical imaging analysis.

关键词： Radiomics feature extraction efficient radiomics features random search-based hyper parameter tuning fine-tuned linear SVM classifier fine-tuned ridge regressor feature selection algorithms prostate cancer classification

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SVR optimization with soft computing algorithms for incipient SGTR diagnosis

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ANNALS OF NUCLEAR ENERGY 2018年 121卷 89-100页

作者： Ayodeji, Abiodun Liu, Yong-kuo Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Heilongjiang Peoples R China Nigeria Atom Energy Commiss Nucl Power Plant Dev Directorate Abuja Nigeria

Fault severity awareness and fault identification are some of the key steps to a successful diagnosis in nuclear power plants. Currently, faults such as leak detection are being done using the N-16 method. However, traditional leak monitors are not sensitive to small leak rate changes, hence cannot be used for low-level leak rate detection under incipient fault conditions and are limited to post-accident analysis of significant releases. In this work, we present a diverse and implementable data-driven Support Vector Regression (SVR) model whose capability compensates for the weaknesses in the already established N-16 methods in the nuclear plant. The method can be integrated with the conventional N-16 method to form a robust hybrid diagnostic system, effective for detecting both incipient and large leakage in the steam generator. The purpose of the SVR model is to estimate uncertain parameters that are sensitive to certain faults, and the parameter estimation efficiency is evaluated using the mean squared error values (MSE). To obtain efficient predictive model capable of supporting decision-making process and to further optimize the model, minimize false alarm rate and reduce computation cost, we also utilized Particle Swarm Optimization algorithm, Sequential feature selection algorithm, and Genetic Algorithm for feature selection purposes. To demonstrate the method and evaluate the predictive model, we simulated steam generator tube rupture (SGTR) faults with varying severity in the reactor coolant system of CNP300 NPP, with RELAPS/SCDAP Mod4.0 code. The SVR's relative error (MSE) with and without feature selection algorithms were compared using different solver algorithms. The feature selection performance of the algorithms and the resulting SVR model fault diagnosis performance evaluation are discussed in this paper. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Fault diagnosis Support vector regression feature selection algorithms Steam generator tube rupture

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Improved feature selection Based On Normalized Mutual Information

Improved Feature Selection Based On Normalized Mutual Inform...

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第十四届分布式计算及其应用国际学术研讨会

作者： LI Yin Ma Xingfei Yang Mengxi Zhao Wei Gu Wenqiang Education Information Research Center WuXi Vocation Institute of Commerce Department I Software Division Shanghai Huaqin Telecom Co.Ltd

For the question(NMIFS) algorithm has the disadvantages of redundancy. This paper introduces a new feature selection method by enhanced NMIFS algorithm. A new quality estimation function is introduced in the new feature selection algorithm to overcome the shortcomings of the classic NMIFS, and the experiment shows on that normalized mutual information feature selection The experiment shows that the INMIFS can generate impressive results in accuracy and redundancy.

关键词： Mutual Information (MI) feature selection algorithms Normalized Mutual Information feature selection(NMIFS)

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