Computational Methods for Preprocessing and Classifying Gene Expression Data- Survey

AL-Mashanji, Ameer K.; AL-Rashi, Sura Z.

doi:10.1109/sicn47020.2019.9019349

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…Microarray technology the coefficients in genes at the same time. Gene expression data for T1D were used in the proposed system [29]. The values obtained by mapping the RMA algorithm using Limma package in R language.…”

Section: Methodsmentioning

confidence: 99%

Classification of gene expression dataset for type 1 diabetes using machine learning methods

AlRefaai

Al-Rashid

2023

Bulletin EEI

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Type 1 diabetes (T1D) disease is considered one of the most prevalent chronic diseases in the world, it causes a high level of glucose in the human blood. Despite the seriousness of this disease, T1D may affect people and their condition develops to an advanced stage without feeling it, which makes it difficult to control the disease. Early prediction of the presence of this disease may significantly reduce its risks. There are many attempts to overcome this disease, some of them are heading towards biological solutions and others towards bioinformatic solutions. Several studies have used a single feature selection method with a machine learning (ML) model to predict or classify T1D. In this paper, ML techniques were used for classification, such as Naive Bayes (NB), support vector machine (SVM), and random forest (RF) using a T1D gene expression dataset that has a multiclass to classify the genes associated with this disease. The proposed model can identify the genes related to T1D with high efficiency, which helps a lot in predicting a person carrying the disease before symptoms appear. The highest accuracy of 89.1% was obtained when applying SVM with chi2 as the feature selection method.

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Section: Methodsmentioning

confidence: 99%

Classification of gene expression dataset for type 1 diabetes using machine learning methods

AlRefaai

Al-Rashid

2023

Bulletin EEI

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“…However, biological TF binding tests are costly and timeconsuming. Interdisciplinary bioinformatics is concerned with the gathering, archiving, and arrangement of biological data as well as its analysis and interpretation [3]. Furthermore, it is described as the application of computer techniques for managing biological data in a different declaration.…”

Section: Introductionmentioning

confidence: 99%

Prediction of DNA Binding Sites Bound to Specific Transcription Factors by the SVM Algorithm

Aziz

Al-Rashid

2022

Iraqi Journal of Science

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In gene regulation, transcription factors (TFs) play a key function. It transmits genetic information from DNA to messenger RNA during the process of DNA transcription. During this step, the transcription factor binds to a segment of the DNA sequence known as Transcription Factor Binding Sites (TFBS). The goal of this study is to build a model that predicts whether or not a DNA binding site attaches to a certain transcription factor (TF). TFs are regulatory molecules that bind to particular sequence motifs in the gene to induce or restrict targeted gene transcription. Two classification methods will be used, which are support vector machine (SVM) and kernel logistic regression (KLR). Moreover, the KLR algorithm depends on another regression algorithm, namely kernel ridge regression (KRR). Discovering binding sites for a transcription factor can help determine genes which it regulates, analyze its functions, comprehend regulation in living organisms, recognize causal disease variations, and, most importantly, manufacture pharmaceutical drugs.

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Gene Expression Dataset Classification Using Machine Learning Methods: A Survey

AlRefaai

Al-Rashid

2022

2022 8th International Conference on Contemporary Information Technology and Mathematics (ICCITM)

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Computational Methods for Preprocessing and Classifying Gene Expression Data- Survey

Cited by 6 publications

References 25 publications

Classification of gene expression dataset for type 1 diabetes using machine learning methods

Classification of gene expression dataset for type 1 diabetes using machine learning methods

Prediction of DNA Binding Sites Bound to Specific Transcription Factors by the SVM Algorithm

Gene Expression Dataset Classification Using Machine Learning Methods: A Survey

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