AIMC Topic: Transcriptome

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TIGERi: modeling and visualizing the responses to perturbation of a transcription factor network.

BMC bioinformatics May 31, 2017
BACKGROUND: Transcription factor (TF) networks play a key role in controlling the transfer of genetic information from gene to mRNA. Much progress has been made on understanding and reverse-engineering TF network topologies using a range of experimen...
Mice, Knockout Binding Sites Mitogen-Activated Protein Kinase 14 Machine Learning Animals Transcriptome Gene Regulatory Networks Mice Transcription Factors
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Drug Target Prediction by Multi-View Low Rank Embedding.

IEEE/ACM transactions on computational biology and bioinformatics May 18, 2017
Drug repositioning has been a key problem in drug development, and heterogeneous data sources are used to predict drug-target interactions by different approaches. However, most of studies focus on a single representation of drugs or proteins. It has...
Humans Machine Learning Algorithms Drug Development Models, Statistical Drug Repositioning Antineoplastic Agents Transcriptome Cell Line, Tumor
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Differential transcriptome analysis supports Rhodnius montenegrensis and Rhodnius robustus (Hemiptera, Reduviidae, Triatominae) as distinct species.

PloS one Apr 13, 2017
Chagas disease is one of the main parasitic diseases found in Latin America and it is estimated that between six and seven million people are infected worldwide. Its etiologic agent, the protozoan Trypanosoma cruzi, is transmitted by triatomines, som...
Male Transcriptome Hemiptera Female Animals Insect Proteins Species Specificity Gene Ontology
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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...
Humans Biomarkers Rats Models, Theoretical Databases, Genetic Animals Male Transcriptome Diabetes Mellitus, Experimental Supervised Machine Learning Rats, Zucker Cluster Analysis Bayes Theorem Gene Expression
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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...
Humans Circadian Clocks Genes Adult Machine Learning Male Transcriptome Female Young Adult
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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...
Up-Regulation Kaplan-Meier Estimate Databases, Genetic Cluster Analysis MicroRNAs Gene Regulatory Networks RNA, Messenger Support Vector Machine Humans Prognosis Colorectal Neoplasms Transcriptome
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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...
Humans Databases, Nucleic Acid Alzheimer Disease Gene Expression Regulation, Bacterial Gene Ontology Transcriptome Salmonella enteritidis
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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...
Neurofibromin 1 Computational Biology Glioblastoma Gene Silencing Humans Machine Learning Transcriptome Cell Line, Tumor
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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...
Support Vector Machine RNA Saccharomyces cerevisiae Adenosine RNA Processing, Post-Transcriptional Transcriptome Computational Biology
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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 ...
Organogenesis Models, Statistical Transcriptome Machine Learning Brain Organ Specificity Algorithms Reproducibility of Results Neurogenesis Single-Cell Analysis Biomarkers Gene Expression Profiling Support Vector Machine
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