mRNA and microRNA selection for breast cancer molecular subtype stratification using meta-heuristic based algorithms

MotieGhader, Habib; Masoudi-Sobhanzadeh, Yosef; Ashtiani, Saman Hosseini; Masoudi‐Nejad, Ali

doi:10.1016/j.ygeno.2020.06.014

Cited by 26 publications

(16 citation statements)

References 87 publications

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“…To classify various types of clinical/biological datasets, the present study introduced a novel wrapper ML method that combined the Trader algorithm for selecting a near-optimal subset of features/genes and the SVM classifier for scoring them. Although the previous studies had suggested several ML methods and algorithms to stratify clinical/biological datasets [ 53 – 55 ], they encountered two critical limitations described as follows.…”

Section: Discussionmentioning

confidence: 99%

A voting-based machine learning approach for classifying biological and clinical datasets

2023

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Background Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, the existing methods suffer from several limitations such as overfitting on a specific dataset, ignoring the feature selection concept in the preprocessing step, and losing their performance on large-size datasets. To tackle the mentioned restrictions, in this study, we introduced a machine learning framework consisting of two main steps. First, our previously suggested optimization algorithm (Trader) was extended to select a near-optimal subset of features/genes. Second, a voting-based framework was proposed to classify the biological/clinical data with high accuracy. To evaluate the efficiency of the proposed method, it was applied to 13 biological/clinical datasets, and the outcomes were comprehensively compared with the prior methods. Results The results demonstrated that the Trader algorithm could select a near-optimal subset of features with a significant level of p-value < 0.01 relative to the compared algorithms. Additionally, on the large-sie datasets, the proposed machine learning framework improved prior studies by ~ 10% in terms of the mean values associated with fivefold cross-validation of accuracy, precision, recall, specificity, and F-measure. Conclusion Based on the obtained results, it can be concluded that a proper configuration of efficient algorithms and methods can increase the prediction power of machine learning approaches and help researchers in designing practical diagnosis health care systems and offering effective treatment plans.

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Section: Discussionmentioning

confidence: 99%

A voting-based machine learning approach for classifying biological and clinical datasets

2023

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“…A feature selection method for binary classification problems was introduced by Dashtban, Balafar & Suravajhala (2018) , in which the traditional bat algorithm is extended with more refined formulations, improved and multi-objective operators and a novel local search strategy. Other examples of feature selection methods could be found in MotieGhader et al (2020) , Dashtban & Balafar (2017) , Nematzadeh et al (2019) , Maghsoudloo et al (2020) , Rostami et al (2020) , Shamsara & Shamsara (2020) , Ao et al (2020) , Statnikov et al (2005) , Rana et al (2019) , Chamikara et al (2016) , Nardone, Ciaramella & Staiano (2019) and the references cited therein.…”

Section: Related Workmentioning

confidence: 99%

Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments

Hamraz

Gul

Raza

et al. 2021

PeerJ Computer Science

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In this paper, a novel feature selection method called Robust Proportional Overlapping Score (RPOS), for microarray gene expression datasets has been proposed, by utilizing the robust measure of dispersion, i.e., Median Absolute Deviation (MAD). This method robustly identifies the most discriminative genes by considering the overlapping scores of the gene expression values for binary class problems. Genes with a high degree of overlap between classes are discarded and the ones that discriminate between the classes are selected. The results of the proposed method are compared with five state-of-the-art gene selection methods based on classification error, Brier score, and sensitivity, by considering eleven gene expression datasets. Classification of observations for different sets of selected genes by the proposed method is carried out by three different classifiers, i.e., random forest, k-nearest neighbors (k-NN), and support vector machine (SVM). Box-plots and stability scores of the results are also shown in this paper. The results reveal that in most of the cases the proposed method outperforms the other methods.

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“…Therefore, miRNA-based therapy could be proposed for SARS-CoV treatment 21 , 22 . On the other hand, the role of pathologic processes in miRNAs, such as inflammatory responses and viral infection, has been recently verified 21 , 23 , 24 .…”

Section: Introductionmentioning

confidence: 99%

Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis

MotieGhader

Safavi

Rezapour

et al. 2021

Sci Rep

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Severe acute respiratory syndrome (SARS) is a highly contagious viral respiratory illness. This illness is spurred on by a coronavirus known as SARS-associated coronavirus (SARS-CoV). SARS was first detected in Asia in late February 2003. The genome of this virus is very similar to the SARS-CoV-2. Therefore, the study of SARS-CoV disease and the identification of effective drugs to treat this disease can be new clues for the treatment of SARS-Cov-2. This study aimed to discover novel potential drugs for SARS-CoV disease in order to treating SARS-Cov-2 disease based on a novel systems biology approach. To this end, gene co-expression network analysis was applied. First, the gene co-expression network was reconstructed for 1441 genes, and then two gene modules were discovered as significant modules. Next, a list of miRNAs and transcription factors that target gene co-expression modules' genes were gathered from the valid databases, and two sub-networks formed of transcription factors and miRNAs were established. Afterward, the list of the drugs targeting obtained sub-networks' genes was retrieved from the DGIDb database, and two drug-gene and drug-TF interaction networks were reconstructed. Finally, after conducting different network analyses, we proposed five drugs, including FLUOROURACIL, CISPLATIN, SIROLIMUS, CYCLOPHOSPHAMIDE, and METHYLDOPA, as candidate drugs for SARS-CoV-2 coronavirus treatment. Moreover, ten miRNAs including miR-193b, miR-192, miR-215, miR-34a, miR-16, miR-16, miR-92a, miR-30a, miR-7, and miR-26b were found to be significant miRNAs in treating SARS-CoV-2 coronavirus.

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mRNA and microRNA selection for breast cancer molecular subtype stratification using meta-heuristic based algorithms

Cited by 26 publications

References 87 publications

A voting-based machine learning approach for classifying biological and clinical datasets

A voting-based machine learning approach for classifying biological and clinical datasets

Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments

Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis

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