A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications

Masoudi-Sobhanzadeh, Yosef; MotieGhader, Habib; Omidi, Yadollah; Masoudi‐Nejad, Ali

doi:10.1038/s41598-021-82796-y

Cited by 14 publications

(6 citation statements)

References 57 publications

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“…As demonstrated in many recent bioinformatics-related pieces of research, wrapper-based FS approaches outperformed the filter and embedded-based FS techniques [ 57 , 58 ], and two-step FS methods usually showed better functionality than single-step procedures [ 59 – 61 ]. Besides, in some cases, previous studies presented that filter-based FS techniques may reduce the prediction power of a learner [ 8 , 62 ]. Hence, given the capabilities of the Trader algorithm in Np-hard problems, this study developed the algorithm for selecting the features and applied it not only to large-size datasets but also to small-size ones.…”

Section: Discussionmentioning

confidence: 99%

“…In this line, several ML strategies have been developed, resulting in the generation of computer-aided health decision support systems [7]. These strategies aimed to improve the ML and feature selection (FS) algorithms mainly because of their effects on the performance of a classification model [8]. For instance, to diagnose diabetes disease in its early stages, Patil et al utilized C4.5 and k-means clustering ML algorithms and achieved ~ 92.38% value of tenfold cross-validation accuracy on the Pima Indian Diabetes (PID) dataset [9].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

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

2023

Self Cite

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“…Most of the previous studies that used GA for gene set selection formulated genes using binary representation in chromosomes [ 75 , 76 , 77 ]. We devised a ternary representation to consider not only selection but also the direction of association for prognosis.…”

Section: Discussionmentioning

confidence: 99%

Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection

Koo

Lee

et al. 2022

Cancers

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Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.

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“…This study employed the Genetic Algorithm (GA) to perform feature reduction for classification purposes. Specifically, traits shown to have no impact on the outcome were eliminated from consideration [29,30].…”

Section: A Data Acquisitionmentioning

confidence: 99%

Enhancement of Recommendation Engine Technique for Bug System Fixes

Al-Bayati,

Al-Shamma,

Tawfeeq

2024

JAIT

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This study aims to develop a recommendation engine methodology to enhance the model's effectiveness and efficiency. The proposed model is commonly used to assign or propose a limited number of developers with the required skills and expertise to address and resolve a bug report. Managing collections within bug repositories is the responsibility of software engineers in addressing specific defects. Identifying the optimal allocation of personnel to activities is challenging when dealing with software defects, which necessitates a substantial workforce of developers. Analyzing new scientific methodologies to enhance comprehension of the results is the purpose of this analysis. Additionally, developer priorities were discussed, especially their utility in allocating a problem to a specific developer. An analysis was conducted on two key areas: first, the development of a model to represent developer prioritizing within the bug repository, and second, the use of hybrid machine learning techniques to select bug reports. Moreover, we use our model to facilitate developer assignment responsibilities. Moreover, we considered the developers' backgrounds and drew upon their established knowledge and experience when formulating the pertinent objectives. An examination of two individuals' experiences with software defects and how their actions impacted their rankings as developers in a software project is presented in this study. Researchers are implementing developer categorization techniques, assessing severity, and reopening bugs. A suitable number of bug reports is used to examine the model's output. A developer's bug assignment employee has been established, enabling the program to successfully address software maintenance issues with the highest accuracy of 78.38%. Best engine performance was achieved by optimizing and cleansing data, using relevant attributes, and processing it using deep learning.

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A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications

Cited by 14 publications

References 57 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

Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection

Enhancement of Recommendation Engine Technique for Bug System Fixes

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