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2. An Efficient High-Dimensional Gene Selection Approach based on Binary Horse Herd Optimization Algorithm for Biological Data Classification

Author

Mehrabi, Niloufar, Boroujeni, Sayed Pedram Haeri, and Pashaei, Elnaz

Subjects
Computer Science - Machine Learning
Abstract

The Horse Herd Optimization Algorithm (HOA) is a new meta-heuristic algorithm based on the behaviors of horses at different ages. The HOA was introduced recently to solve complex and high-dimensional problems. This paper proposes a binary version of the Horse Herd Optimization Algorithm (BHOA) in order to solve discrete problems and select prominent feature subsets. Moreover, this study provides a novel hybrid feature selection framework based on the BHOA and a minimum Redundancy Maximum Relevance (MRMR) filter method. This hybrid feature selection, which is more computationally efficient, produces a beneficial subset of relevant and informative features. Since feature selection is a binary problem, we have applied a new Transfer Function (TF), called X-shape TF, which transforms continuous problems into binary search spaces. Furthermore, the Support Vector Machine (SVM) is utilized to examine the efficiency of the proposed method on ten microarray datasets, namely Lymphoma, Prostate, Brain-1, DLBCL, SRBCT, Leukemia, Ovarian, Colon, Lung, and MLL. In comparison to other state-of-the-art, such as the Gray Wolf (GW), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), the proposed hybrid method (MRMR-BHOA) demonstrates superior performance in terms of accuracy and minimum selected features. Also, experimental results prove that the X-Shaped BHOA approach outperforms others methods.

Published
2023

4. A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems

Author

Boroujeni, Sayed Pedram Haeri and Pashaei, Elnaz

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. They are designed to escape from a local optimum problem by allowing flexible movements or random behaviors. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of ChOA with two different independent groups' strategies and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Secondly, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. The results are evaluated against seven popular meta-heuristic optimization algorithms and eight recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the achievement in minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest Error Rate (ER), accelerating the convergence speed, and finding the optimal cluster centers., Comment: 48 pages, 14 Tables, 12 Figures

Published
2023
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11. Hybrid Krill Herd Algorithm with Particle Swarm Optimization for Image Enhancement

Author

Pashaei, Elnaz, Pashaei, Elham, Aydin, Nizamettin, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Kahraman, Cengiz, editor, Cevik Onar, Sezi, editor, Oztaysi, Basar, editor, Sari, Irem Ucal, editor, Cebi, Selcuk, editor, and Tolga, A. Cagri, editor

Published
2021
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12. CDHu40: a novel marker gene set of neuroendocrine prostate cancer (NEPC)

Author

Liu, Sheng, primary, Nam, Hye Seung, additional, Zeng, Ziyu, additional, Deng, Xuehong, additional, Pashaei, Elnaz, additional, Zang, Yong, additional, Yang, Lei, additional, Li, Chenglong, additional, Huang, Jiaoti, additional, Wendt, Michael K, additional, Lu, Xin, additional, Huang, Rong, additional, and Wan, Jun, additional

Published
2024
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24. A hybrid chimp optimization algorithm and generalized normal distribution algorithm with opposition-based learning strategy for solving data clustering problems

Author

Haeri Boroujeni, Sayed Pedram and Pashaei, Elnaz

Abstract

This paper focuses on connectivity-based data clustering for categorizing similar and dissimilar data into distinct groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. First, two versions of ChOA with two different dynamic coefficients and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Second, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. In this study, eight benchmark datasets, including five datasets of the UCI machine learning repository and three challenging shape datasets, are used to investigate the general performance of the proposed method. The results are evaluated against several popular and recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the Sum of Intra-Cluster Distances (SICD), Error Rate (ER), and convergence rate.

Published
2023
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27. Meta-analysis of microRNA and gene selection using machine learning

Author

Pashaei, Elnaz, Aydın, Nizamettin, and Bilgisayar Mühendisliği Anabilim Dalı

Subjects
Computer Engineering and Computer Science and Control, Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol
Abstract

DNA mikrodizi teknolojisi doku örneklerinde çok sayıda genin ifade düzeyini aynı anda izlemeyi ve ölçmeyi mümkün kılar. Mikrodizi veri setlerinde örnek sayısı gen sayısından çok daha azdır. Bu tür verilerin sınıflandırılması bilinen `boyutsallık belası (curse of dimentionality)` ve veri aşırı uyumluluk problemiyle sonuçlanır. Başarılı bir hastalık teşhisi için, sınıflandırma ile alakalı az sayıda ayrımcı gen seçmek gerekir. Mikrodizi veri analizinde gen seçimi sadece sınıflandırma doğruluğunu arttırmakla kalmaz, aynı zamanda klinik ortamda işleme süresini azaltır. Bu nedenle, başarılı bir hastalık teşhis sistemi geliştirmek için genlerin minimum bir alt kümesini belirlemek oldukça önemlidir. Bu tezde, melez doğadan esinlenmiş optimizasyon algoritmaların ve farklı sınıflayıcılara dayanan kanser sınıflandırmasında yüksek derecede ayırıcı gen seçimi için iki yaklaşım önerilmiştir. İlk önerilen yaklaşımda Kara Delik Algoritması, ilk defa bir özellik seçimi (FS) problemini çözmek için kullanılmaktadır. Hiperbolik teğet fonksiyonunu uygulayarak, metin, görüntü ve biyomedikal verilerin FS'sini çözmek için BHA'nın BBHA adlı yeni bir iki tabanlı biçimi kullanılır. İki sınıflayıcı (RF ve NB) önerilen algoritmamızın değerlendiricileri olarak görev yapmaktadır. Deneysel sonuçlar BBHA sarmalayıcı (wrapper) temelli özellik seçim yönteminin tüm kriterler açısından BPSO, GA, SA ve CFS'den üstün olduğunu göstermektedir. BBHA, CPU Zamanı, modeli yapılandırma parametrelerinin sayısı ve seçilen en iyileştirilmiş özelliklerin sayısı açısından BPSO ve GA'ya göre önemli ölçüde daha iyi bir performans sunar. Ayrıca, BBHA, literatürdeki diğer yöntemlere kıyasla rekabetçi veya daha iyi bir performansa sahiptir. Önerilen ikinci yaklaşımda, İkili Parçacık Sürüsü Optimizasyonunun (BPSO) performansını iyileştiriyoruz ve BPSO için yerel iyileştirici olarak BBHA uygulayarak yerel bir optimumda sıkışmayı önlemeye yardımcı oluyoruz. Dört klinik veri kümesindeki deneysel sonuçlar ve istatistiksel analiz, önerilen yöntemin, ateş böceği, karınca koloni, yarasa arama, genetik algoritma, armoni araştırması, hızlı korelasyon tabanlı süzgeç ve korelasyon tabanlı özellik alt küme seçimi gibi yaklaşımlara göre önemli derecede daha iyi sınıflandırma performansı elde ederken çok küçük bilgi grubu genleri ürettiğini göstermektedir. Dahası, BPSO için yerel iyileştirici olarak BBHA'nın uygulanmasının BPSO'nun performansını belirgin bir şekilde artırabileceği ve yerel optimumda sıkışmayı önlemesine yardımcı olacağı da gösterildi.Prostat kanseri reküransında çeşitli miRNA ifade veri setleri yapılmıştır. Bununla birlikte, sonuçlar farklı çalışmalar arasında çeşitlilik göstermektedir. Bireysel çalışmaları entegre ederek istatistiksel güç artar ve daha güvenilir sonuçlar ve yeni biyolojik bilgiler elde edilebilir. Bu tezde, radikal prostatektomiden sonra prostat kanseri reküransı için altı mevcut miRNA ifade veri seti üzerinde bir meta-analiz yaptık ve potansiyel olarak farklı olarak eksprese edilen mikroRNA genlerinin önemli bir listesini tespit ettik. Tanımlanmış mikroRNA genlerinin katıldığı moleküler yolakları tanımlamak ve nükseden prostat kanseri üzerinde ilaç tedavileri için yeni yönergeler ortaya çıkarmak için gen ontolojisi zenginleştirmesi, KEGG analizi ve ortak yolakanalizi yaptık.Önemli bir tümör baskılayıcı mikroRNA olan MiR-145, birçok kanser çeşidinde downregüle edildiğini ve tümörün başlatılması, progresyonu, metastazı, invazyonu, reküransı ve kemoradyolojik direncinde önemli rollere sahip olduğunu göstermiştir. Sekiz GEO veri kümesinin meta-analizi ile bu tezde, ortak hedef genlerle bağlantılı olarak tümör patogenezinin altında yatan moleküler yolaklarının anlaşılmasına yardımcı olmak için, miR-145'in potansiyel ortak hedef genlerini araştırdık. The DNA microarray technology allows for monitoring and measuring the expression level of a great number of genes in tissue samples simultaneously. In microarray datasets, the number of samples is much smaller than the number of genes. The classification of such data resulting in the known problem of `curse of dimensionality` and data overfitting. For a successful disease diagnosis, it is necessary to select a small number of discriminating genes that are relevant for classification. Gene selection in microarray data analysis not only increases the classification accuracy but also decreases the processing time in the clinical setting. Therefore, it is quite important to determine a minimum subset of genes to develop a successful disease diagnostic system. In this thesis, two approaches for selecting highly discriminating genes in cancer classification based on a hybrid of nature-inspired optimization algorithms and different classifiers are proposed. In the first proposed approach, Black Hole Algorithm is, for the first time, being used to solve a feature selection (FS) problem. By applying the hyperbolic tangent function, a new binary version of BHA called BBHA is utilized to solve FS in the text, image, and biomedical data. Two classifiers (RF and NB) serve as the evaluators of our proposed algorithm. Experimental results show that BBHA wrapper-based feature selection method is superior to BPSO, GA, SA, and CFS in terms of all criteria. BBHA gives a significantly better performance than the BPSO and GA in terms of CPU Time, the number of parameters for configuring the model, and the number of chosen optimized features. Also, BBHA has competitive or better performance than the other methods in the literature. In the second proposed approach, we improve the performance of Binary Particle Swarm Optimization (BPSO) and help it to avoid being trapped in a local optimum by applying BBHA as the local optimizer for BPSO. Experimental results and statistical analysis on four clinical datasets demonstrate that the proposed method yields very small subsets of informative genes, while achieving significantly better classification performance than other approaches such as Firefly, ant colony, bat search, genetic algorithm, harmony search, Fast Correlation-Based Filter (FCBF), and Correlation-based Feature Subset Selection (CFS). Moreover, It was also shown that applying BBHA as the local optimizer for BPSO can significantly improve the performance of BPSO and help it to avoid being trapped in a local optimum.Several studies on miRNA expression datasets have been conducted in prostate cancer recurrence. However, the results have varied among different studies. By integrating the individual studies the statistical power is increased and more reliable conclusions and new biological insights can be drawn. In this thesis, we conducted a meta-analysis on six available miRNA expression datasets for prostate cancer recurrence after radical prostatectomy and identified a potentially significant list of differentially expressed microRNA genes. We did gene ontology enrichment, KEGG analysis, and common pathway analysis to identify the molecular pathways in which the identified microRNA genes participate and reveal new directions for drug treatments of recurrent prostate cancer.MiR-145, an important tumor suppressor microRNA, has shown to be downregulated in many cancer types and has crucial roles in tumor initiation, progression, metastasis, invasion, recurrence, and chemoradioresistance. In this thesis by meta-analysis of eight GEO datasets, we investigated potential common target genes of miR-145 to help to understand the underlying molecular pathways of tumor pathogenesis in association with those common target genes. 123

Published
2017

32. Multiclass Cancer Diagnosis using Firefly Algorithm and K-Nearest Neighbor.

Author

PASHAEI, Elnaz

Subjects
CANCER diagnosis, ALGORITHMS, K-nearest neighbor classification
Abstract

Among a large number of genes in microarray data sets that characterize the samples, many of them may be irrelevant to the learning tasks. Thus there is a need for reliable methods for gene representation, reduction, and selection, to speed up the processing rate, improve the classification accuracy, and to avoid incomprehensibility due to the high number of genes investigated. Classifying multiclass data sets is usually more difficult than classifying microarray datasets with only two classes. In this paper, we propose a new gene selection and classification strategy based on Firefly Algorithm (FFA) and K-Nearest Neighbor (KNN), suitable for multiclass microarray data sets. This approach is associated with Kruskal-test pre-filtering technique. The FFA is utilized to evolve gene subsets whose fitness is evaluated by a KNN classifier with leave-one-out-cross-validation (LOOCV) schema. The experimental results on three multiclass high-dimensional data sets show that the proposed method simplifies gene signatures effectively and obtains approximately higher classification accuracy compared to the best previously published results. [ABSTRACT FROM AUTHOR]

Published
2018

37. CDHu40: a novel marker gene set of neuroendocrine prostate cancer.

Author

Liu S, Nam HS, Zeng Z, Deng X, Pashaei E, Zang Y, Yang L, Li C, Huang J, Wendt MK, Lu X, Huang R, and Wan J

Subjects
Humans, Male, Prognosis, Gene Expression Regulation, Neoplastic, Prostatic Neoplasms genetics, Prostatic Neoplasms pathology, Prostatic Neoplasms metabolism, Protein Interaction Maps, Gene Expression Profiling, Prostatic Neoplasms, Castration-Resistant genetics, Prostatic Neoplasms, Castration-Resistant pathology, Prostatic Neoplasms, Castration-Resistant metabolism, Computational Biology methods, Carcinoma, Neuroendocrine genetics, Carcinoma, Neuroendocrine pathology, Carcinoma, Neuroendocrine metabolism, Biomarkers, Tumor genetics
Abstract

Prostate cancer (PCa) is the most prevalent cancer affecting American men. Castration-resistant prostate cancer (CRPC) can emerge during hormone therapy for PCa, manifesting with elevated serum prostate-specific antigen levels, continued disease progression, and/or metastasis to the new sites, resulting in a poor prognosis. A subset of CRPC patients shows a neuroendocrine (NE) phenotype, signifying reduced or no reliance on androgen receptor signaling and a particularly unfavorable prognosis. In this study, we incorporated computational approaches based on both gene expression profiles and protein-protein interaction networks. We identified 500 potential marker genes, which are significantly enriched in cell cycle and neuronal processes. The top 40 candidates, collectively named CDHu40, demonstrated superior performance in distinguishing NE PCa (NEPC) and non-NEPC samples based on gene expression profiles. CDHu40 outperformed most of the other published marker sets, excelling particularly at the prognostic level. Notably, some marker genes in CDHu40, absent in the other marker sets, have been reported to be associated with NEPC in the literature, such as DDC, FOLH1, BEX1, MAST1, and CACNA1A. Importantly, elevated CDHu40 scores derived from our predictive model showed a robust correlation with unfavorable survival outcomes in patients, indicating the potential of the CDHu40 score as a promising indicator for predicting the survival prognosis of those patients with the NE phenotype. Motif enrichment analysis on the top candidates suggests that REST and E2F6 may serve as key regulators in the NEPC progression., (© The Author(s) 2024. Published by Oxford University Press.)

Published
2024
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38. CDHu40: a novel marker gene set of neuroendocrine prostate cancer (NEPC).

Author

Liu S, Nam HS, Zeng Z, Deng X, Pashaei E, Zang Y, Yang L, Li C, Huang J, Wendt MK, Lu X, Huang R, and Wan J

Abstract

Prostate cancer (PCa) is the most prevalent cancer affecting American men. Castration-resistant prostate cancer (CRPC) can emerge during hormone therapy for PCa, manifesting with elevated serum prostate-specific antigen (PSA) levels, continued disease progression, and/or metastasis to the new sites, resulting in a poor prognosis. A subset of CRPC patients shows a neuroendocrine (NE) phenotype, signifying reduced or no reliance on androgen receptor (AR) signaling and a particularly unfavorable prognosis. In this study, we incorporated computational approaches based on both gene expression profiles and protein-protein interaction (PPI) networks. We identified 500 potential marker genes, which are significantly enriched in cell cycle and neuronal processes. The top 40 candidates, collectively named as CDHu40, demonstrated superior performance in distinguishing NE prostate cancer (NEPC) and non-NEPC samples based on gene expression profiles compared to other published marker sets. Notably, some novel marker genes in CDHu40, absent in the other marker sets, have been reported to be associated with NEPC in the literature, such as DDC, FOLH1, BEX1, MAST1, and CACNA1A. Importantly, elevated CDHu40 scores derived from our predictive model showed a robust correlation with unfavorable survival outcomes in patients, indicating the potential of the CDHu40 score as a promising indicator for predicting the survival prognosis of those patients with the NE phenotype. Motif enrichment analysis on the top candidates suggests that REST and E2F6 may serve as key regulators in the NEPC progression., Significance: our study integrates gene expression variances in multiple NEPC studies and protein-protein interaction network to pinpoint a specific set of NEPC maker genes namely CDHu40. These genes and scores based on their gene expression levels effectively distinguish NEPC samples and underscore the clinical prognostic significance and potential mechanism.

Published
2024
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39. Biomarker discovery based on BBHA and AdaboostM1 on microarray data for cancer classification.

Author

Pashaei E, Ozen M, and Aydin N

Subjects
Decision Trees, Gene Expression Profiling, Humans, Algorithms, Biomarkers, Tumor genetics, Computational Biology methods, Neoplasms classification, Neoplasms genetics, Oligonucleotide Array Sequence Analysis, Statistics as Topic methods
Abstract

In this paper, a new approach based on Binary Black Hole Algorithm (BBHA) and Adaptive Boosting version Ml (AdaboostM1) is proposed for finding genes that can classify the group of cancers correctly. In this approach, BBHA is used to perform gene selection and AdaboostM1 with 10-fold cross validation is adopted as the classifier. Also, to find the relation between the biomarkers for biological point of view, decision tree algorithm (C4.5) is utilized. The proposed approach is tested on three benchmark microarrays. The experimental results show that our proposed method can select the most informative gene subsets by reducing the dimension of the data set and improve classification accuracy as compared to several recent studies.

Published
2016
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40. Improving medical diagnosis reliability using Boosted C5.0 decision tree empowered by Particle Swarm Optimization.

Author

Pashaei E, Ozen M, and Aydin N

Subjects
Algorithms, Databases, Factual, Female, Humans, Reproducibility of Results, Sensitivity and Specificity, Decision Trees, Diagnostic Techniques and Procedures, Machine Learning
Abstract

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods.

Published
2015
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