Tuning parameter estimation in SCAD-support vector machine using firefly algorithm with application in gene selection and cancer classification

Al-Thanoon, Niam Abdulmunim; Qasim, Omar Saber; Algamal, Zakariya Yahya

doi:10.1016/j.compbiomed.2018.10.034

Cited by 51 publications

(23 citation statements)

References 31 publications

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“…In their demonstration, they were able to classify cardiac activity of unknown compounds with an accuracy of roughly 72% and generalize the results to other drug families with an accuracy above 70% [218]. Further, ML and its myriad algorithms can also be used on the protein and gene side of tissue engineering, as it has been demonstrated or proposed for histopathological image analysis [43], ligand affinity [42], folding structure [219], gene expression and biomarker data mining [220, 221], and in evaluation of pre-implantation embryos [222]. Large datasets such as the “Tissue Atlas” [223], a human proteome map categorized by tissue, could easily be used as a training and testing set for ML algorithms targeting identification of impaired tissue or disease onset.…”

Section: Machine Learning and Precision Control For 3d Scaffold Fabrimentioning

confidence: 99%

Cardiac tissue engineering: state-of-the-art methods and outlook

et al. 2019

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The purpose of this review is to assess the state-of-the-art fabrication methods, advances in genome editing, and the use of machine learning to shape the prospective growth in cardiac tissue engineering. Those interdisciplinary emerging innovations would move forward basic research in this field and their clinical applications. The long-entrenched challenges in this field could be addressed by novel 3-dimensional (3D) scaffold substrates for cardiomyocyte (CM) growth and maturation. Stem cell-based therapy through genome editing techniques can repair gene mutation, control better maturation of CMs or even reveal its molecular clock. Finally, machine learning and precision control for improvements of the construct fabrication process and optimization in tissue-specific clonal selections with an outlook of cardiac tissue engineering are also presented.

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Section: Machine Learning and Precision Control For 3d Scaffold Fabrimentioning

confidence: 99%

Cardiac tissue engineering: state-of-the-art methods and outlook

et al. 2019

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“…FA has been remained a successful metaheuristic in health care domain. In the year 2018, Al-Thanoon et al [142] has proposed a modern way of approach for establishing the parameter tuning in SCAD (Smoothly Clipped Absolute Deviation) penalty as well as PSVM using FA. This method has been applied on the selection of genes as well SN Computer Science as classification of cancer.…”

Section: Fa In Health Carementioning

confidence: 99%

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Nayak

Naik

Dinesh³

et al. 2020

SN COMPUT. SCI.

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Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA.

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“…The feature selection method reduces the number of features used to describe a set of big datasets in order to improve the performance of the algorithm. The aim of this method is to reduce the number of features used and reduce the time required to obtain results, which leads to an increase in the accuracy of classi ication [18,21] (Figure 3). The feature selection algorithm can be represented in three stages:…”

Section: Features Selectionmentioning

confidence: 99%

Classification of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm

AL-Taie¹,

Qasim²

2020

Ann Proteom Bioinform

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Genetic datasets have a large number of features that may signifi cantly affect the disease classifi cation process, especially datasets related to cancer diseases. Evolutionary algorithms (EA) are used to fi nd the fastest and best way to perform these calculations, such as the bat algorithm (BA) by reducing the dimensions of the search area after changing it from continuous to discrete. In this paper, a method of gene selection was proposed two sequent stages: in the fi rst stage, the fuzzy mutual information (FMI) method is used to choose the most important genes selected through a fuzzy model that was built based on the dataset size. In the second stage, the BBA is used to reduce and determine a fi xed number of genes affecting the process of classifi cation, which came from the fi rst stage. The proposed algorithm, FMI_BBA, describes effi ciency, by obtaining a higher classifi cation accuracy and a few numbers of selected genes compared to other algorithms.How to cite this article: Yonis AL-Taie FA, Qasim OS. Classifi cation of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm. Ann Proteom Bioinform. 2020; 4: 001-005.

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Tuning parameter estimation in SCAD-support vector machine using firefly algorithm with application in gene selection and cancer classification

Cited by 51 publications

References 31 publications

Cardiac tissue engineering: state-of-the-art methods and outlook

Cardiac tissue engineering: state-of-the-art methods and outlook

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Classification of diseases using a hybrid fuzzy mutual information technique with binary bat algorithm

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