AIMC Topic: Transcriptome

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Transcriptomes of lineage-specific Drosophila neuroblasts profiled by genetic targeting and robotic sorting.

Development (Cambridge, England) Dec 23, 2015
A brain consists of numerous distinct neurons arising from a limited number of progenitors, called neuroblasts in Drosophila. Each neuroblast produces a specific neuronal lineage. To unravel the transcriptional networks that underlie the development ...
Gene Targeting Drosophila melanogaster Cell Lineage Gene Expression Regulation, Developmental Single-Cell Analysis Sequence Analysis, RNA Transcription Factors Animals Animals, Genetically Modified Transcriptome Drosophila Proteins Robotics Neurons
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Predicting target proteins for drug candidate compounds based on drug-induced gene expression data in a chemical structure-independent manner.

BMC medical genomics Dec 18, 2015
BACKGROUND: Phenotype-based high-throughput screening is a useful technique for identifying drug candidate compounds that have a desired phenotype. However, the molecular mechanisms of the hit compounds remain unknown, and substantial effort is requi...
Humans Machine Learning Neoplasms Drug Screening Assays, Antitumor MCF-7 Cells Structure-Activity Relationship HL-60 Cells Antineoplastic Agents Transcriptome Gene Expression Regulation, Neoplastic
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Putative synaptic genes defined from a Drosophila whole body developmental transcriptome by a machine learning approach.

BMC genomics Sep 15, 2015
BACKGROUND: Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Although roughly a thousand genes are expected to be important for this function in Drosophila melanogaster, just a few hund...
Transcriptome Computational Biology Rats Models, Biological Algorithms Animals Gene Expression Profiling Drosophila Gene Expression Regulation, Developmental Synapses Datasets as Topic Organ Specificity Humans Machine Learning
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Seq-ing improved gene expression estimates from microarrays using machine learning.

BMC bioinformatics Sep 4, 2015
BACKGROUND: Quantifying gene expression by RNA-Seq has several advantages over microarrays, including greater dynamic range and gene expression estimates on an absolute, rather than a relative scale. Nevertheless, microarrays remain in widespread use...
Humans Machine Learning Sequence Analysis, RNA High-Throughput Nucleotide Sequencing Gene Expression Profiling Transcriptome Computational Biology
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OVA: integrating molecular and physical phenotype data from multiple biomedical domain ontologies with variant filtering for enhanced variant prioritization.

Bioinformatics (Oxford, England) Aug 12, 2015
MOTIVATION: Exome sequencing has become a de facto standard method for Mendelian disease gene discovery in recent years, yet identifying disease-causing mutations among thousands of candidate variants remains a non-trivial task.
Genotype Computational Biology Biological Ontologies Disease Exome Phenotype Transcriptome Polymorphism, Single Nucleotide Humans Algorithms
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Computational assignment of cell-cycle stage from single-cell transcriptome data.

Methods (San Diego, Calif.) Jul 2, 2015
The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in para...
Embryonic Stem Cells Transcriptome Cell Line, Tumor Single-Cell Analysis Humans Hepatocytes Animals Mice Machine Learning Computational Biology Cell Cycle Gene Expression Profiling
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A tuberculosis ontology for host systems biology.

Tuberculosis (Edinburgh, Scotland) Jun 30, 2015
A major hurdle facing tuberculosis (TB) investigators who want to utilize a rapidly growing body of data from both systems biology approaches and omics technologies is the lack of a standard vocabulary for data annotation and reporting. Lacking a mea...
Transcriptome Systems Biology Databases, Genetic Mycobacterium tuberculosis Gene Expression Regulation Humans Host-Pathogen Interactions Genetic Markers Terminology as Topic Animals Tuberculosis Gene Ontology Gene Expression Profiling
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Computer vision for image-based transcriptomics.

Methods (San Diego, Calif.) May 23, 2015
Single-cell transcriptomics has recently emerged as one of the most promising tools for understanding the diversity of the transcriptome among single cells. Image-based transcriptomics is unique compared to other methods as it does not require conver...
Gene Expression Profiling Transcriptome Single-Cell Analysis Humans Artificial Intelligence Algorithms Animals
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De novo transcriptome assembly of pummelo and molecular marker development.

PloS one Mar 23, 2015
Pummelo (Citrus grandis) is an important fruit crop worldwide because of its nutritional value. To accelerate the pummelo breeding program, it is essential to obtain extensive genetic information and develop relative molecular markers. Here, we obtai...
Physical Chromosome Mapping Nucleotide Motifs Gene Library Microsatellite Repeats Cluster Analysis Gene Expression Profiling Genetic Markers Transcriptome High-Throughput Nucleotide Sequencing Molecular Sequence Annotation Phenotype Polymorphism, Single Nucleotide Gene Frequency Gene Ontology Reproducibility of Results Citrus Phylogeny
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Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

Computational biology and chemistry Mar 18, 2015
Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene...
Humans Artificial Intelligence Algorithms Neoplasms Transcriptome Oligonucleotide Array Sequence Analysis
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