Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson’s Diseases and Improving the Disease Classification Using Support Vector Machine

Moradi, Shadi; Tapak, Leili; Afshar, Saeid

doi:10.1155/2022/5009892

Cited by 26 publications

(10 citation statements)

References 44 publications

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“…Cheng et al [ 101 ] suggested that GNG11 could be used as a biomarker for differentiate ulcerative colitis and Crohn’s disease. Moradi et al [ 102 ] proposed that GNG11 could be a diagnostic biomarker for Parkinson’s disease. GNG11 plays a key role in heart rhythm regulation and is associated with cardiac disease risk [ 103 ].…”

Section: Discussionmentioning

confidence: 99%

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Karaman

Işık

2023

Medical Sciences

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Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.

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Section: Discussionmentioning

confidence: 99%

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Karaman

Işık

2023

Medical Sciences

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“…In 2022, Moradi et al [19] offered a microarray dataset (GSE22491) that was given by GEO. The Limma package, which is part of the R program, was used to find DEGs and analyze and assess gene expression.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Method for Detecting Parkinson using 1D Convolutional Neural Networks and Improved Jellyfish Algorithms

Victor Paul M,

Shankar

2024

Int. j. electr. comput. eng. syst. (Online)

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Parkinson's disease (PD) is a common disease that predominantly impacts the motor scheme of the neural central scheme. While the primary symptoms of Parkinson's disease overlap with those of other conditions, an accurate diagnosis typically relies on extensive neurological, psychiatric, and physical examinations. Consequently, numerous autonomous diagnostic assistance systems, based on machine learning (ML) methodologies, have emerged to assist in evaluating patients with PD. This work proposes a novel deep learning-based classification of Parkinson's disease (PD) using voice recordings of people into normal, idiopathic Parkinson, and familial Parkinson. The improved jellyfish algorithm (IJFA) is utilized for hyper-parameter selection (HPS) of a 1D convolutional neural network (1D-CNN). The proposed technique makes use of the significant elements of 1D-CNN and filter-based feature selection models. Because of their strong performance in dealing with noisy data, the filter-based algorithms Relief, mRMR, and Fisher Score were chosen as the top choices. Using just 62 characteristics, the combination of deep relief features and deep learning was able to discriminate between people. The competence of the proposed 1D-CNN with IJFA method was determined through specific network metrics. The proposed 1D-CNN with IJFA method attains a total accuracy of 98.6%, which is comparatively better than the existing techniques. The proposed model produced around 9.5% improvements in accuracy, respectively, when compared to the data obtained without dimensionality reduction.

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“…Establishment of the SVM model SVM is a supervised ML algorithm that is mainly used for data categorization [33]. This algorithm distinguishes sample type by estimating the degree of a sample that belongs to a speci c class [34].…”

Section: Evaluating the Performance Of Feature Selection Techniquesmentioning

confidence: 99%

Identifying metastatic biomarkers of colorectal cancer: Machine learning modeling and experimental validation

Ahmadieh-Yazdi

Mahdavinezhad

Tapak

et al. 2023

Preprint

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Background Colorectal cancer (CRC) liver metastasis is responsible for the majority of CRC-related deaths. Early detection of metastasis is crucial for improving patient outcomes but can be delayed due to a lack of symptoms. In this research, we aimed to investigate for CRC metastasis related biomarkers by employing machine learning (ML) approach and experimental validation. Methods Gene expression profile of CRC patients with liver metastasis was obtained using GSE41568 dataset and the differentially expressed genes between primary and metastatic samples were screened. Subsequently, we carried out feature selection to identify most relevant DEGs using LASSO and Penalized-SVM methods. DEGs commonly selected by these methods were selected for further analysis. Finally, the experimental validation was done through qRT-PCR. Results 11 genes were commonly selected by SCAD and P-SVM algorithms among which seven had prognostic value in colorectal cancer. It was found that the expression of MMP3 gene decreases in stage IV of colorectal cancer compared to other stages (p-value < 0.01). Also, the expression of WNT11gene increases significantly in this stage(p-value < 0.001). It was also found that expression of WNT5a, TNFSF11 and MMP3 is significantly lower, and the expression level of WNT11 is significantly higher in liver metastasis samples compared to primary tumors. Conclusion In summary, this study has identified a set of potential biomarkers for CRC metastasis using ML algorithms. The findings of this research may provide new insights into the identification of biomarkers for CRC metastasis and may lead to new therapeutic strategies for the treatment of this disease.

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Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson’s Diseases and Improving the Disease Classification Using Support Vector Machine

Cited by 26 publications

References 44 publications

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers

Deep Learning-Based Method for Detecting Parkinson using 1D Convolutional Neural Networks and Improved Jellyfish Algorithms

Identifying metastatic biomarkers of colorectal cancer: Machine learning modeling and experimental validation

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