AIMC Topic: Gene Expression Profiling
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Protein-protein interaction network construction for cancer using a new L1/2-penalized Net-SVM model.
Genetics and molecular research : GMR
Jul 25, 2016
Identifying biomarker genes and characterizing interaction pathways with high-dimensional and low-sample size microarray data is a major challenge in computational biology. In this field, the construction of protein-protein interaction (PPI) networks...
Consistent quantitative gene product expression: #1. Automated identification of regenerating bone marrow cell populations using support vector machines.
Cytometry. Part A : the journal of the International Society for Analytical Cytology
Jul 14, 2016
Identification and quantification of maturing hematopoietic cell populations in flow cytometry data sets is a complex and sometimes irreproducible step in data analysis. Supervised machine learning algorithms present promise to automatically classify...
NeuroRDF: semantic integration of highly curated data to prioritize biomarker candidates in Alzheimer's disease.
Journal of biomedical semantics
Jul 8, 2016
BACKGROUND: Neurodegenerative diseases are incurable and debilitating indications with huge social and economic impact, where much is still to be learnt about the underlying molecular events. Mechanistic disease models could offer a knowledge framewo...
Integrated gene set analysis for microRNA studies.
Bioinformatics (Oxford, England)
Jun 20, 2016
MOTIVATION: Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limita...
Ensemble Feature Learning of Genomic Data Using Support Vector Machine.
PloS one
Jun 15, 2016
The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the proc...
A knowledge-based approach for predicting gene-disease associations.
Bioinformatics (Oxford, England)
Jun 9, 2016
MOTIVATION: Recent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat de...
A kernel-based clustering method for gene selection with gene expression data.
Journal of biomedical informatics
May 20, 2016
Gene selection is important for cancer classification based on gene expression data, because of high dimensionality and small sample size. In this paper, we present a new gene selection method based on clustering, in which dissimilarity measures are ...
Integrated Analysis of Expression Profile Based on Differentially Expressed Genes in Middle Cerebral Artery Occlusion Animal Models.
International journal of molecular sciences
May 20, 2016
Stroke is one of the most common causes of death, only second to heart disease. Molecular investigations about stroke are in acute shortage nowadays. This study is intended to explore a gene expression profile after brain ischemia reperfusion. Meta-a...
Clustering Single-Cell Expression Data Using Random Forest Graphs.
IEEE journal of biomedical and health informatics
May 10, 2016
Complex tissues such as brain and bone marrow are made up of multiple cell types. As the study of biological tissue structure progresses, the role of cell-type-specific research becomes increasingly important. Novel sequencing technology such as sing...
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.
Nucleic acids research
May 3, 2016
Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gen...
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