AIMC Topic: Transcriptome

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Transcriptomics and machine learning predict diagnosis and severity of growth hormone deficiency.

JCI insight Apr 5, 2018
BACKGROUND: The effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and rando...
Human Growth Hormone Growth Disorders Child, Preschool Child Transcriptome Case-Control Studies Adolescent Predictive Value of Tests Principal Component Analysis Healthy Volunteers Datasets as Topic ROC Curve Gene Regulatory Networks Polymorphism, Single Nucleotide Humans Severity of Illness Index Machine Learning Gene Expression Profiling Biomarkers Female Male
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Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Apr 4, 2018
The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main advantages: 1) it provides the ability to learn very complex non-linear relationships between features ...
Humans Neural Networks, Computer Gene Expression Profiling Transcriptome Diagnosis, Computer-Assisted Deep Learning Software Algorithms Neoplasms Area Under Curve Linear Models Pattern Recognition, Automated Gene Expression Regulation Genomics Computational Biology Models, Statistical
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A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets.

BMC bioinformatics Mar 27, 2018
BACKGROUND: Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes...
Gene Ontology Quantitative Trait Loci Models, Theoretical Bayes Theorem Markov Chains Humans Algorithms ROC Curve Models, Genetic Computer Simulation Transcriptome
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2D-EM clustering approach for high-dimensional data through folding feature vectors.

BMC bioinformatics Dec 28, 2017
BACKGROUND: Clustering methods are becoming widely utilized in biomedical research where the volume and complexity of data is rapidly increasing. Unsupervised clustering of patient information can reveal distinct phenotype groups with different under...
Humans Cluster Analysis Algorithms Transcriptome
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Unsupervised Network Analysis of the Plastic Supraoptic Nucleus Transcriptome Predicts Caprin2 Regulatory Interactions.

eNeuro Dec 21, 2017
The supraoptic nucleus (SON) is a group of neurons in the hypothalamus responsible for the synthesis and secretion of the peptide hormones vasopressin and oxytocin. Following physiological cues, such as dehydration, salt-loading and lactation, the SO...
Transcriptome Supraoptic Nucleus Data Mining Real-Time Polymerase Chain Reaction Gene Expression Regulation Female Animals Microarray Analysis Computational Biology RNA-Binding Proteins Male PC12 Cells RNA, Messenger Rats Unsupervised Machine Learning
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Identification of human circadian genes based on time course gene expression profiles by using a deep learning method.

Biochimica et biophysica acta. Molecular basis of disease Dec 12, 2017
Circadian genes express periodically in an approximate 24-h period and the identification and study of these genes can provide deep understanding of the circadian control which plays significant roles in human health. Although many circadian gene ide...
Humans Machine Learning Databases, Genetic Circadian Rhythm Gene Expression Profiling Transcriptome
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Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine.

Biochimica et biophysica acta. Molecular basis of disease Dec 12, 2017
Hematopoiesis is a complicated process involving a series of biological sub-processes that lead to the formation of various blood components. A widely accepted model of early hematopoiesis proceeds from long-term hematopoietic stem cells (LT-HSCs) to...
Hematopoietic Stem Cells Support Vector Machine Databases, Genetic Gene Expression Regulation Humans Gene Expression Profiling Transcriptome
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Large-scale automated function prediction of protein sequences and an experimental case study validation on PTEN transcript variants.

Proteins Nov 29, 2017
Recent advances in computing power and machine learning empower functional annotation of protein sequences and their transcript variations. Here, we present an automated prediction system UniGOPred, for GO annotations and a database of GO term predic...
Humans Machine Learning PTEN Phosphohydrolase Databases, Protein Gene Ontology Animals Models, Biological Sequence Analysis, Protein Transcriptome Proteomics
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Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems.

PLoS computational biology Nov 17, 2017
Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series ...
Models, Genetic HeLa Cells Saccharomyces cerevisiae Cell Cycle Gene Silencing Algorithms Gene Expression Profiling Transcriptome Computational Biology Humans Neural Networks, Computer
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Structured Penalized Logistic Regression for Gene Selection in Gene Expression Data Analysis.

IEEE/ACM transactions on computational biology and bioinformatics Oct 30, 2017
In gene expression data analysis, the problems of cancer classification and gene selection are closely related. Successfully selecting informative genes will significantly improve the classification performance. To identify informative genes from a l...
Neoplasms Gene Expression Profiling Databases, Genetic Humans Transcriptome Computational Biology Machine Learning Cluster Analysis Logistic Models
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