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Articles 1 - 4 of 4
Full-Text Articles in Microarrays
Classification Of Coronary Artery Disease In Non-Diabetic Patients Using Artificial Neural Networks, Demond Handley
Classification Of Coronary Artery Disease In Non-Diabetic Patients Using Artificial Neural Networks, Demond Handley
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Non-Invasive Analysis Of The Sputum Transcriptome Discriminates Clinical Phenotypes Of Asthma, Xiting Yan
Non-Invasive Analysis Of The Sputum Transcriptome Discriminates Clinical Phenotypes Of Asthma, Xiting Yan
Yale Day of Data
Whole transcriptome wide gene expression profiles in the sputum and circulation from 100 asthma patients were measured using the Affymetrix HuGene 1.0ST arrays. Unsupervised clustering analysis based on pathways from KEGG were used to identify TEA clusters of patients from the sputum gene expression profiles. The identified TEA clusters have significantly different pre-bronchodilator FEV1, bronchodilator responsiveness, exhaled nitric oxide levels, history of hospitalization for asthma and history of intubation. Evaluation of TEA clusters in children from Asthma BRIDGE cohort confirmed the identified differences in intubation and hospitalization. Furthermore, evaluation of the TH2 gene signatures suggested a much lower prevalence of …
A Novel Pathway-Based Distance Score Enhances Assessment Of Disease Heterogeneity In Gene Expression, Yunqing Liu, Xiting Yan
A Novel Pathway-Based Distance Score Enhances Assessment Of Disease Heterogeneity In Gene Expression, Yunqing Liu, Xiting Yan
Yale Day of Data
Distance-based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and the relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. In this study, we developed a novel computational method to assess the biological differences based on pathways by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both …
A Comparison Of Unsupervised Methods For Dna Microarray Leukemia Data, Denise Harness
A Comparison Of Unsupervised Methods For Dna Microarray Leukemia Data, Denise Harness
Appalachian Student Research Forum
Advancements in DNA microarray data sequencing have created the need for sophisticated machine learning algorithms and feature selection methods. Probabilistic graphical models, in particular, have been used to identify whether microarrays or genes cluster together in groups of individuals having a similar diagnosis. These clusters of genes are informative, but can be misleading when every gene is used in the calculation. First feature reduction techniques are explored, however the size and nature of the data prevents traditional techniques from working efficiently. Our method is to use the partial correlations between the features to create a precision matrix and predict which …