AI Medical Compendium Topic

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Databases, Genetic

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Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality.

BMC bioinformatics
BACKGROUND: In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatically increased. From clinical variables and PET-CT radiomics measures to DNA-variant and RNA expressio...

Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.

BMC bioinformatics
BACKGROUND: Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discove...

A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation.

Cell
Alternative polyadenylation (APA) is a major driver of transcriptome diversity in human cells. Here, we use deep learning to predict APA from DNA sequence alone. We trained our model (APARENT, APA REgression NeT) on isoform expression data from over ...

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse.

Neuron
Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders ("synaptopathies"). However, systematic annotation of synaptic genes and ontology of synaptic processes are currently la...

Computational determination of hERG-related cardiotoxicity of drug candidates.

BMC bioinformatics
BACKGROUND: Drug candidates often cause an unwanted blockage of the potassium ion channel of the human ether-a-go-go-related gene (hERG). The blockage leads to long QT syndrome (LQTS), which is a severe life-threatening cardiac side effect. Therefore...

Local online learning in recurrent networks with random feedback.

eLife
Recurrent neural networks (RNNs) enable the production and processing of time-dependent signals such as those involved in movement or working memory. Classic gradient-based algorithms for training RNNs have been available for decades, but are inconsi...

Type-2 Fuzzy PCA Approach in Extracting Salient Features for Molecular Cancer Diagnostics and Prognostics.

IEEE transactions on nanobioscience
Machine learning is becoming a powerful tool for cancer diagnosis and prognosis based on classification using high dimensional molecular data. However, extracting classification features from high-dimensional datasets remains a challenging problem. P...

HOME: a histogram based machine learning approach for effective identification of differentially methylated regions.

BMC bioinformatics
BACKGROUND: The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate...