AI Medical Compendium Topic

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Genome

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ARIADNA: machine learning method for ancient DNA variant discovery.

DNA research : an international journal for rapid publication of reports on genes and genomes
Ancient DNA (aDNA) studies often rely on standard methods of mutation calling, optimized for high-quality contemporary DNA but not for excessive contamination, time- or environment-related damage of aDNA. In the absence of validated datasets and desp...

A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data.

Bioinformatics (Oxford, England)
MOTIVATION: Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared with the huge amount of features (p). This 'n≪p' property has hampered application of deep learning techn...

LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Nucleosome positioning plays significant roles in proper genome packing and its accessibility to execute transcription regulation. Despite a multitude of nucleosome positioning resources available on line including experimental datasets o...

Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information.

Annals of oncology : official journal of the European Society for Medical Oncology
BACKGROUND: Tumor-specific mutations form novel immunogenic peptides called neoantigens. Neoantigens can be used as a biomarker predicting patient response to cancer immunotherapy. Although a predicted binding affinity (IC50) between peptide and majo...

Genome-wide pre-miRNA discovery from few labeled examples.

Bioinformatics (Oxford, England)
MOTIVATION: Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative ex...

Complementary Sources of Protein Functional Information: The Far Side of GO.

Methods in molecular biology (Clifton, N.J.)
The GO captures many aspects of functional annotations, but there are other alternative complementary sources of protein function information. For example, enzyme functional annotations are described in a range of resources from the Enzyme Commission...

Evaluating Computational Gene Ontology Annotations.

Methods in molecular biology (Clifton, N.J.)
Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computati...

Higher order methylation features for clustering and prediction in epigenomic studies.

Bioinformatics (Oxford, England)
MOTIVATION: DNA methylation is an intensely studied epigenetic mark, yet its functional role is incompletely understood. Attempts to quantitatively associate average DNA methylation to gene expression yield poor correlations outside of the well-under...