Diagnosis of coronary heart diseases using gene expression profiling; stable coronary artery disease, cardiac ischemia with and without myocardial necrosis

Kazmi, Nabila; Gaunt, Tom R.

doi:10.1101/019505

Cited by 2 publications

(2 citation statements)

References 25 publications

(14 reference statements)

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“…We aim to support precision medicine in HF and other CVDs by investigating genes associated with CVDs and analyzing their variants and expression levels that correlate with disease phenotype. RNA-seq-driven gene expression analysis has the potential to reveal novel and sensitive biomarkers and stratify CVD patient populations based on their disease risk [11]. While DNA-based gene variant analysis can assist in diagnosis and plan personalized treatments, integrating and/or validating sequential—one after another—gene expression and variant analyses will certainly advance our understanding of CVD biology.…”

Section: Introductionmentioning

confidence: 99%

Investigating variant and expression of CVD genes associated phenotypes among high-risk Heart Failure patients

Ahmed

Zeeshan

Persaud

et al. 2023

Preprint

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Cardiovascular disease (CVD) is a leading cause of premature mortality in the US and the world. CVD comprises of several complex and mostly heritable conditions, which range from myocardial infarction to congenital heart disease. Here, we report our findings from an integrative analysis of gene expression, disease-causing gene variants, and associated phenotypes among CVD populations, with a focus on high-risk Heart Failure (HF) patients. We built a cohort using electronic health records (EHR) of consented patients with available samples, and then performed high-throughput whole-genome and RNA sequencing (RNA-seq) of key genes responsible for HF and other CVD pathologies. We also incorporated a translational aspect to our study by integrating genomics findings with patient medical records. This involved linking ICD-10 codes with our gene expression and variant data to identify associations with HF and other CVDs. Our in-depth gene expression analysis revealed differentially expressed genes associated with HF (41 genes) and other CVDs (23 genes). Furthermore, a variant analysis of whole-genome sequence data of CVD patients identified genes with altered gene expression (FLNA, CST3, LGALS3, and HBA1) with functional and nonfunctional mutations in these genes. Our study highlights the importance of an integrative approach that leverages gene expression, genetic mutations, and clinical data that will allow the prioritization of key driver genes for complex diseases to improve personalized healthcare.

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Section: Introductionmentioning

confidence: 99%

Investigating variant and expression of CVD genes associated phenotypes among high-risk Heart Failure patients

Ahmed

Zeeshan

Persaud

et al. 2023

Preprint

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“…84% of overlapping with heart, indicating that study of peripheral blood transcriptase can be an economic and readily accessible tool for proxy gene expression in other tissues [17]. Though many studies have focused in the domain of differential expression in CVD outcomes, in [18], the authors have focused on using differential expression for classifying the patient record outcomes. Such an approach provides efficacy to improve the diagnosis to sub-classify patients.…”

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confidence: 99%

Genome feature optimization and coronary artery disease prediction using cuckoo search

Neelima

Babu

2020

Comput. Sci. Inf. Technol.

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CVD (Cardiovascular Diseases) is among the major health ailment issue leading to millions of deaths every year. CVDs are resulting as an outcome of implications in terms of environmental and the genetic factors that result in the CVD for individuals. Phenomenal advancements that has taken place in the diagnosis solutions like usage of genomic tools are contributing towards predicting and diagnosis of heart diseases more accurately. In recent past, analyzing gene expression data, particularly using machine learning strategies to predict and classify the given unlabeled gene expression record is a generous research issue. Concerning this, a substantial requirement is feature optimization, which is since the overall genes observed in human body are closely 25000 and among them 636 are cardio vascular related genes. Hence, it complexes the process of training the machine learning models using these entire cardio vascular gene features. Hence, this manuscript is using bidirectional pooled variance strategy of ANOVA standard to select optimal features. Along the side to surpass the constraint observed in traditional classifiers, which is unstable accuracy at k-fold cross validation, this manuscript proposed a classification strategy that build upon the swarm intelligence technique called cuckoo search. The experimental study indicating that the number of optimal features those selected by proposed model is substantially low that compared to the other contemporary model that selects features using Forward Feature Selection and classifies using SVM classifier (FFSSVM). The experimental study evinced that the proposed model, which selects feature by bidirectional pooled variance estimation and classifies using proposed classification strategy that build on cuckoo search (BPVECS) outperformed the selected contemporary model (FFSSVM).

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Diagnosis of coronary heart diseases using gene expression profiling; stable coronary artery disease, cardiac ischemia with and without myocardial necrosis

Cited by 2 publications

References 25 publications

Investigating variant and expression of CVD genes associated phenotypes among high-risk Heart Failure patients

Investigating variant and expression of CVD genes associated phenotypes among high-risk Heart Failure patients

Genome feature optimization and coronary artery disease prediction using cuckoo search

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