AIMC Topic: Transcriptome

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SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles.

Nucleic acids research May 7, 2019
Characterization of individual cell types is fundamental to the study of multicellular samples. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task....
Single-Cell Analysis Mice Sequence Analysis, RNA Gene Expression Regulation, Neoplastic Animals Gene Expression Profiling Transcriptome High-Throughput Nucleotide Sequencing Datasets as Topic Cell Lineage RNA, Small Cytoplasmic Cluster Analysis Pancreatic Neoplasms Supervised Machine Learning Humans Software
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WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach.

Nucleic acids research Apr 23, 2019
N 6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotes, and plays a pivotal role in various biological processes, such as splicing, RNA degradation and RNA-protein interaction. We report here a prediction fram...
Internet RNA High-Throughput Nucleotide Sequencing Transcriptome Methylation Protein Interaction Maps Adenosine Humans Sequence Analysis, RNA Machine Learning Epigenesis, Genetic
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Predicting drug synergy for precision medicine using network biology and machine learning.

Journal of bioinformatics and computational biology Apr 1, 2019
Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effecti...
Humans Machine Learning Protein Interaction Maps Computational Biology Drug Synergism Gene Ontology Precision Medicine Reproducibility of Results Transcriptome Databases, Pharmaceutical
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Deep learning in omics: a survey and guideline.

Briefings in functional genomics Feb 14, 2019
Omics, such as genomics, transcriptome and proteomics, has been affected by the era of big data. A huge amount of high dimensional and complex structured data has made it no longer applicable for conventional machine learning algorithms. Fortunately,...
Proteomics Guidelines as Topic Surveys and Questionnaires Genomics Computational Biology Transcriptome Deep Learning Humans Algorithms
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The Splicing Code Goes Deep.

Cell Jan 24, 2019
The importance of genomic sequence context in generating transcriptome diversity through RNA splicing is independently unmasked by two studies in this issue (Jaganathan et al., 2019; Baeza-Centurion et al., 2019).
RNA Splicing Transcriptome Deep Learning Genomics Genome
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Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2019
The cost of new drug development has been increasing, and repurposing known medications for new indications serves as an important way to hasten drug discovery. One promising approach to drug repositioning is to take advantage of machine learning (ML...
Supervised Machine Learning Gene Expression Regulation Databases, Pharmaceutical Computational Biology Drug Repositioning Workflow Transcriptome Deep Learning Genomics Humans Neural Networks, Computer Reproducibility of Results Machine Learning Gene Expression Profiling Algorithms
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L1000FWD: fireworks visualization of drug-induced transcriptomic signatures.

Bioinformatics (Oxford, England) Jun 15, 2018
MOTIVATION: As part of the NIH Library of Integrated Network-based Cellular Signatures program, hundreds of thousands of transcriptomic signatures were generated with the L1000 technology, profiling the response of human cell lines to over 20 000 sma...
Humans Software Cluster Analysis Cell Line Transcriptome Pharmacogenetics Unsupervised Machine Learning Gene Expression Regulation Data Visualization
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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cell Apr 5, 2018
Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovati...
Cell Dedifferentiation Machine Learning Tumor Microenvironment Neoplasms MicroRNAs Carcinogenesis Transcriptome Epigenesis, Genetic Stem Cells Humans Databases, Genetic Neoplasm Metastasis DNA Methylation
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Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2017
Although contemporary high-throughput -omics methods produce high-dimensional data, the resulting wealth of information is difficult to assess using traditional statistical procedures. Machine learning methods facilitate the detection of additional p...
Genomics Computational Biology Cluster Analysis Periodontitis Gene Expression Profiling Algorithms Humans Periodontal Diseases Machine Learning Transcriptome Phenotype Gene Expression Regulation Software Genome-Wide Association Study
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FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model.

Oncotarget Jul 19, 2016
Accumulating experimental studies have indicated the influence of lncRNAs on various critical biological processes as well as disease development and progression. Calculating lncRNA functional similarity is of high value in inferring lncRNA functions...
Fuzzy Logic RNA, Long Noncoding Transcriptome Humans Gene Regulatory Networks Computer Simulation Computational Biology Gene Expression Profiling
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