Identification of risk genes related to myocardial infarction and the construction of early SVM diagnostic model

Song, Xiaolong; Zheng, Yuanyuan; Xue, Wenhua; Li, Lifeng; Shen, Zhibo; Ding, Xianfei; Zhai, Yunkai; Zhao, Jie

doi:10.1016/j.ijcard.2020.12.007

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…This idea is also integrated into every corner of machine learning. SVM, as one of the machine learning algorithms, has been widely used in various classification and recognition problems [5][6][7] [8]. Support Vector Regression (SVR) is developed based on SVM, which is mainly used in data estimation, fitting, and regression prediction.…”

Section: Development Of Machine Learningmentioning

confidence: 99%

Deep Learning-based Analysis of Voiceprint Data Mining

Tang¹

2022

JSER

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In the information age, the intelligent data mining method represented by deep learning is playing an important role in various fields at present. It is necessary to study how to efficiently use the intelligent data mining method to obtain valuable information from massive information. Open-set voiceprint recognition is realized by intelligent data mining technology. Therefore, it is of great practical significance to achieve rapid and accurate identification of the speaker's identity. Because the traditional voiceprint recognition method has insufficient ability to distinguish the speakers inside and outside the set, it often leads to a high false recognition rate. Mining parameters containing more speakers' personality characteristics and how to calculate the threshold become the bottleneck problems of open set voiceprint recognition. Therefore, this paper adopts the deep confidence network stacked by three layers of restricted Boltzmann machines as the deep acoustic feature extractor. The mel-frequency cepstral coefficients of 24-dimensional basic acoustic features are mapped to 256dimensional feature space, and the parameters of deep acoustic features containing more speaker's personality characteristics are obtained. Then, an open-set adaptive threshold calculation algorithm is obtained. In this paper, the similarity value of deep acoustic features is calculated by the Gaussian mixture model, and the maximum inter-class variance of the similarity value is calculated by the OTSU algorithm. When the inter-class variance is the maximum, the similarity value is the best threshold. The experimental test shows that the algorithm for calculating threshold based on deep learning proposed in this paper has a lower false rejection rate and lower false rejection rate.

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Section: Development Of Machine Learningmentioning

confidence: 99%

Deep Learning-based Analysis of Voiceprint Data Mining

Tang¹

2022

JSER

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“…However, early MI has lesions such as coronary arteries, blood composition, and hormone levels. Therefore, MI can be prevented at an early stage by molecular level changes as markers ( 16 ). Recent studies in transcriptomics have been continuously applied in clinical practice, especially in routine diagnostic applications.…”

Section: Introductionmentioning

confidence: 99%

Hypoxia-associated genes predicting future risk of myocardial infarction: a GEO database-based study

Zhang

et al. 2023

Front. Cardiovasc. Med.

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BackgroundPatients with unstable angina (UA) are prone to myocardial infarction (MI) after an attack, yet the altered molecular expression profile therein remains unclear. The current work aims to identify the characteristic hypoxia-related genes associated with UA/MI and to develop a predictive model of hypoxia-related genes for the progression of UA to MI.Methods and resultsGene expression profiles were obtained from the GEO database. Then, differential expression analysis and the WGCNA method were performed to select characteristic genes related to hypoxia. Subsequently, all 10 hypoxia-related genes were screened using the Lasso regression model and a classification model was established. The area under the ROC curve of 1 shows its excellent classification performance and is confirmed on the validation set. In parallel, we construct a nomogram based on these genes, showing the risk of MI in patients with UA. Patients with UA and MI had their immunological status determined using CIBERSORT. These 10 genes were primarily linked to B cells and some inflammatory cells, according to correlation analysis.ConclusionOverall, GWAS identified that the CSTF2F UA/MI risk gene promotes atherosclerosis, which provides the basis for the design of innovative cardiovascular drugs by targeting CSTF2F.

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“…Supervised machine learning (ML) models used in biological research aid in the discover of molecules and establishment of dynamic models that recognize, classify, and predict disease outcomes 40,41,42,43,44 . In recent years, studies have employed the use of ML framework to identify candidate biomarkers for disease classi cation, cell and tumor expression signatures, and novel protein mechanisms within publicly available RNA-Seq datasets 45,46,47,48,49 . However, to our knowledge, the use of ML-based methodology has not been explored with BRD-associated datasets.…”

Section: Introductionmentioning

confidence: 99%

Genes and Mechanisms Associated With Experimentally Induced Bovine Respiratory Disease Identified With Supervised Machine Learning Methodology on Integrated Transcriptomic Datasets

Scott¹,

Woolums²,

Swiderski³

et al. 2021

Preprint

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Bovine respiratory disease (BRD) is a multifactorial disease involving complex host immune interactions shaped by pathogenic agents and environmental factors. Advancements in RNA sequencing and associated analytical methods are improving our understanding of host response related to BRD pathophysiology. Supervised machine learning (ML) approaches present one such method for analyzing new and previously published transcriptome data to identify novel genes and mechanisms. Our objective was to apply ML models to lung and immunological tissue datasets acquired from previous clinical BRD experiments to identify genes that classify disease with high accuracy. Raw mRNA sequencing reads from 151 bovine datasets (n=123 BRD, n=28 control) were downloaded from NCBI-GEO. Quality filtered reads were assembled in a HISAT2/Stringtie2 pipeline. Raw gene counts for ML analysis were normalized, transformed, and analyzed with MLSeq, utilizing six ML models. Cross-validation parameters (5-fold, repeated 10 times) were applied in a 70:30 training/testing ratio. Downstream analysis of genes identified by the top sparse classifiers for each etiological association was performed within WebGestalt and Reactome (FDR < 0.05). Support vector machines was routinely the top non-sparse classifier for predicting etiological disease versus sham control. Nearest shrunken centroid and Poisson linear discriminant analysis with power transformation could reliably classify IBR and BRSV with 100% accuracy. Genes identified in IBR and BRSV, but not BVDV, were related to type I interferon production and IL-8 secretion, specifically in lymphoid tissue and not lung. Genes identified in Mannheimia haemolytica infections were involved in activating classical and alternative pathways of complement. Novel findings, including expression of genes related to reduced mitochondrial oxygenation and ATP synthesis in consolidated lung tissue, were discovered. Genes identified in each analysis represent distinct genomic events relevant to understanding and predicting clinical BRD. The few genes shared across analyses may be reliably associated with clinical BRD. Our analysis demonstrates the utility of ML with published datasets for discovering functional information to support prediction and understanding BRD acquisition.

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Identification of risk genes related to myocardial infarction and the construction of early SVM diagnostic model

Cited by 4 publications

References 34 publications

Deep Learning-based Analysis of Voiceprint Data Mining

Deep Learning-based Analysis of Voiceprint Data Mining

Hypoxia-associated genes predicting future risk of myocardial infarction: a GEO database-based study

Genes and Mechanisms Associated With Experimentally Induced Bovine Respiratory Disease Identified With Supervised Machine Learning Methodology on Integrated Transcriptomic Datasets

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