Comparative Study of Classification Algorithms for Various DNA Microarray Data

Kim, Jingeun; Yoon, Yourim; Park, Hye-Jin; Kim, Yong-Hyuk

doi:10.3390/genes13030494

Cited by 14 publications

(4 citation statements)

References 47 publications

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“…They absorb the beneficial electrons in our body and create disease. It is also the cause of various chronic diseases [19][20]. Antioxidants have the power to reduce its severity.…”

Section: Introductionmentioning

confidence: 99%

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Logeshwaran¹,

Adhikari

Joshi³

et al. 2022

ijhs

156

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In general, the various medical systems currently available provide insights into changes in the tumor genome of patients with tumor sequencing. Most of the tumor DNA sequencing can also be referred to as genetic specification or genetic testing. The sequence results help clinical decision-making to develop a personalized cancer treatment plan based on the molecular characteristics of the tumor rather than a one-size-fits-all treatment approach. The tumor sequencing also plays a major role in cancer research. In this paper, an improved method based on machine learning was proposed to analyze the sequencing and tumor sequencing patterns of the human gene. This proposed method analyzes the circulatory problems of patients with different tumor types for analysis in the public domain. It also constantly monitors large data sets of cancer or tumor genetic sequences to calculate tumor size and location. This allows the doctor to get an accurate report on the type of tumor and the problems it can cause to the patient. The Analysis of these datasets of cancer tumor gene sequences reveals that the genetic makeup of each patient is different and that no two cancers are the same.

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“…They absorb the beneficial electrons in our body and create disease. It is also the cause of various chronic diseases [19][20]. Antioxidants have the power to reduce its severity.…”

Section: Introductionmentioning

confidence: 99%

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Logeshwaran¹,

Adhikari

Joshi³

et al. 2022

ijhs

156

View full text Add to dashboard Cite

show abstract

“…Clustering algorithms such as k-means, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN) have played a pivotal role in discerning genetic variations and uncovering potential associations with diseases [ 28 ]. Classification algorithms like support vector machines (SVMs), decision trees, and Bayesian networks have further augmented the analysis, facilitating tasks such as phenotype prediction, identification of disease markers, and classification of diverse genetic conditions [ 29 ]. The integration of these AI techniques into genomic data analysis has not only propelled our comprehension of the genetic underpinnings of diseases but also holds tremendous promise for advancing personalized medicine and driving the development of tailored therapeutic interventions [ 30 ].…”

Section: Genomic Data Analysis Using Ai Techniquesmentioning

confidence: 99%

Artificial intelligence in Immuno-genetics

Farzan

2024

Bioinformation

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Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.

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“…It is well known that some optimizers that improve and stabilize the learning rates of MLP include momentum, nesterovated gradient, stochastic gradient descent, and adaptive moment estimation (Adam). Adam is applied in our study, because of its low memory needs, great computational efficiency, and scalability with big datasets [26]. Since a learning rate of 0.01 is known to help preventing underfitting, it is the default value for this parameter, which regulates the step size in weight updates.…”

Section: Classificationmentioning

confidence: 99%

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

El-Gawady¹,

Tawfik²,

Makhlouf³

2023

Computers, Materials &Amp; Continua

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Gene expression (GE) classification is a research trend as it has been used to diagnose and prognosis many diseases. Employing machine learning (ML) in the prediction of many diseases based on GE data has been a flourishing research area. However, some diseases, like Alzheimer's disease (AD), have not received considerable attention, probably owing to data scarcity obstacles. In this work, we shed light on the prediction of AD from GE data accurately using ML. Our approach consists of four phases: preprocessing, gene selection (GS), classification, and performance validation. In the preprocessing phase, gene columns are preprocessed identically. In the GS phase, a hybrid filtering method and embedded method are used. In the classification phase, three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase. The final phase is to validate the performance of these classifiers using different metrics. The crux of this article is to select the most informative genes from the hybrid method, and the best ML technique to predict AD using this minimal set of genes. Five different datasets are used to achieve our goal. We predict AD with impressive values for MultiLayer Perceptron (MLP) classifier which has the best performance metrics in four datasets, and the Support Vector Machine (SVM) achieves the highest performance values in only one dataset. We assessed the classifiers using seven metrics; and received impressive results, allowing for a credible performance rating. The metrics values we obtain in our study lie in the range [.97, .99] for the accuracy (Acc), [.97, .99] for F1score, [.94, .98] for kappa index, [.97, .99] for area under curve (AUC), [.95, 1] for precision, [.98, .99] for sensitivity (recall), and [.98, 1] for specificity. With these results, the proposed approach outperforms recent interesting results. With these results, the proposed approach outperforms recent interesting results.

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Comparative Study of Classification Algorithms for Various DNA Microarray Data

Cited by 14 publications

References 47 publications

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

deep DNA machine learning model to classify the tumor genome of patients with tumor sequencing

Artificial intelligence in Immuno-genetics

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

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