AI Medical Compendium Topic

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Genome

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Scaling tree-based automated machine learning to biomedical big data with a feature set selector.

Bioinformatics (Oxford, England)
MOTIVATION: Automated machine learning (AutoML) systems are helpful data science assistants designed to scan data for novel features, select appropriate supervised learning models and optimize their parameters. For this purpose, Tree-based Pipeline O...

Machine learning meets genome assembly.

Briefings in bioinformatics
MOTIVATION: With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health scie...

HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data.

Bioinformatics (Oxford, England)
MOTIVATION: High-resolution Hi-C data are indispensable for the studies of three-dimensional (3D) genome organization at kilobase level. However, generating high-resolution Hi-C data (e.g. 5 kb) by conducting Hi-C experiments needs millions of mammal...

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype.

Bioinformatics (Oxford, England)
MOTIVATION: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have...

Deep learning with multimodal representation for pancancer prognosis prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Estimating the future course of patients with cancer lesions is invaluable to physicians; however, current clinical methods fail to effectively use the vast amount of multimodal data that is available for cancer patients. To tackle this p...

Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships.

Bioinformatics (Oxford, England)
MOTIVATION: Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predic...

Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species.

Bioinformatics (Oxford, England)
MOTIVATION: As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) is recently shown to play crucial roles in restriction-modification systems. For better understanding of their functional mechanisms, it is fundamentally important ...

DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions.

Bioinformatics (Oxford, England)
MOTIVATION: Recognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have...

ME-Class2 reveals context dependent regulatory roles for 5-hydroxymethylcytosine.

Nucleic acids research
Since the discovery of 5-hydroxymethylcytosine (5hmC) as a prominent DNA modification found in mammalian genomes, an emergent question has been what role this mark plays in gene regulation. 5hmC is hypothesized to function as an intermediate in the d...

The Splicing Code Goes Deep.

Cell
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).