AIMC Topic: Transcriptome
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Two-way learning with one-way supervision for gene expression data.
BMC bioinformatics
Mar 4, 2017
BACKGROUND: A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix...
Machine learning identifies a compact gene set for monitoring the circadian clock in human blood.
Genome medicine
Feb 28, 2017
BACKGROUND: The circadian clock and the daily rhythms it produces are crucial for human health, but are often disrupted by the modern environment. At the same time, circadian rhythms may influence the efficacy and toxicity of therapeutics and the met...
Identification of miRNA-mRNA Modules in Colorectal Cancer Using Rough Hypercuboid Based Supervised Clustering.
Scientific reports
Feb 21, 2017
Differences in the expression profiles of miRNAs and mRNAs have been reported in colorectal cancer. Nevertheless, information on important miRNA-mRNA regulatory modules in colorectal cancer is still lacking. In this regard, this study presents an app...
A novel hypothesis-unbiased method for Gene Ontology enrichment based on transcriptome data.
PloS one
Feb 15, 2017
Gene Ontology (GO) classification of statistically significantly differentially expressed genes is commonly used to interpret transcriptomics data as a part of functional genomic analysis. In this approach, all significantly expressed genes contribut...
A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma.
BMC genomics
Feb 6, 2017
BACKGROUND: We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging be...
Detecting N-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines.
Scientific reports
Jan 12, 2017
As one of the most abundant RNA post-transcriptional modifications, N-methyladenosine (mA) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. Howe...
A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.
BMC genomics
Dec 22, 2016
BACKGROUND: The ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a ...
On the inconsistency of ℓ -penalised sparse precision matrix estimation.
BMC bioinformatics
Dec 13, 2016
BACKGROUND: Various ℓ -penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation and learning of undirected network structure from data. Many of these methods have been shown to be consisten...
Transcriptome assists prognosis of disease severity in respiratory syncytial virus infected infants.
Scientific reports
Nov 11, 2016
Respiratory syncytial virus (RSV) causes infections that range from common cold to severe lower respiratory tract infection requiring high-level medical care. Prediction of the course of disease in individual patients remains challenging at the first...
Comprehensive analysis of epigenetically regulated genes in anergic T cells.
Cellular immunology
Nov 2, 2016
T cell anergy is one of the important mechanisms for immune tolerance. The results of many studies investigating the mechanism for T cell anergy induction have revealed that the expression of several genes was up-regulated in anergic T cells. It has ...
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