AIMC Topic: Genome

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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).

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...