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Showing 1 to 20 of 240 for “"Gene Expression Data"”.
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Computational analysis of gene expression data
Gene expression is central to the function of living cells. While advances in sequencing and expression measurement technology over the past decade has greatly facilitated the further understanding of the genome and its functions, the characterisation of functional groups of genes remains one of …
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Inference from binary gene expression data
… measuring the mRNA abundances of thousands of genes in a single experiment. Analysing such large dimensional data is a challenge which attracts researchers from many different fields and machine learning is one of them. However, the biological properties of mRNA such as its low stability, …
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INFERRING TUMOR PROPERTIES FROM GENE EXPRESSION DATA
The wide availability of transcriptomics data, along with the advancement of experimental technologies, computational tools, and statistical methods like machine learning (ML), has significantly improved the use of transcriptome data in cancer research, benefiting both diagnostic and prognostic …
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Drug Repurposing Using Gene Expression Data Mining
… overall cost and risk.</p> <p>Drug-perturbed gene expression profiles are powerful phenotype readouts of biological systems, and they have been widely used in drug repurposing studies. However, the existing drug-perturbed gene expression datasets are extremely noisy and the profiling is …
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APPLY DATA CLUSTERING TO GENE EXPRESSION DATA
<p>Data clustering plays an important role in effective analysis of gene expression. Although DNA microarray technology facilitates expression monitoring, several challenges arise when dealing with gene expression datasets. Some of these challenges are the enormous number of genes, the …
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Quantile Regression Approach For Analyzing Gene Expression Data
Temporal gene expression data contains ample information to characterize gene function and is now widely used in bio-medical research. A dense temporal gene ex- pression usually shows various patterns in expression levels under dfferent biological conditions. Existing literature models the gene …
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Deep learning benchmarks on L1000 gene expression data
Gene expression data holds the potential to offer deep, physiological insights about the dynamic state of a cell beyond the static coding of the genome alone. I believe that realizing this potential requires specialized machine learning methods capable of using underlying biological structure, but …
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Robust Fuzzy Cluster Ensemble on Cancer Gene Expression Data
… growth in the scale and complexity of biological data generated by emerging high-throughput biotechnologies, including gene expression data generated by microarray technology. High-throughput gene expression data may contain gene expression measurements of thousands or millions of genes in a …
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Biologically-Interpretable Disease Classification Based on Gene Expression Data
Classification of tissues and diseases based on gene expression data is a powerful application of DNA microarrays. Many popular classifiers like support vector machines, nearest-neighbour methods, and boosting have been applied successfully to this problem. However, it is difficult to determine …
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Clustering of Leukemia Patients via Gene Expression Data Analysis
… to cluster some leukemia patients described by gene expression data, and discover the most discriminating a few genes that are responsible for the clustering. A combined approach of Principal Direction Divisive Partitioning and bisect K-means algorithms is applied to the clustering of the …
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Survival Prediction For Brain Tumor Patients Using Gene Expression Data
… In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built …
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Statistical Methods for the Analysis of Contextual Gene Expression Data
Technological advances have enabled profiling gene expression variability, both at the RNA and the protein level, with ever increasing throughput. In addition, miniaturisation has enabled quantifying gene expression from small volumes of the input material and most recently at the level of single …
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Feature selection for cancer classification using microarray gene expression data
… technology enables researchers to measure the expression levels of thousands of genes simultaneously and allows biologists easily gain insight into the complex interaction in tumours on gene expression levels. Its application in cancer studies has been shown great success in both diagnosis and …
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A study of computational methods to analyze gene expression data
… technologies has led to huge amounts of genomic data. With these data come new opportunities to understand biological cellular processes underlying hidden regulation mechanisms and to identify disease related biomarkers for informative diagnostics. However, extracting biological insights from the …
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Learning Statistical and Geometric Models from Microarray Gene Expression Data
… dissertation, we propose and develop innovative data modeling and analysis methods for extracting meaningful and specific information about disease mechanisms from microarray gene expression data. To provide a high-level overview of gene expression data for easy and insightful understanding of …
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Microarray gene expression data analysis using machine learning and neural networks
… provides an effective way to measure the expression levels of tens of thousands of genes simultaneously under different conditions, which makes it possible to investigate the gene activities of the whole genome. However, computational challenges have to be faced as a result of the large …
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