AIMC Topic: Genome

Clear Filters Showing 71 to 80 of 178 articles

Identifying transcriptomic correlates of histology using deep learning.

PloS one
Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide ...

Deep learning approach for predicting functional Z-DNA regions using omics data.

Scientific reports
Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using ...

A Multivariate Poisson Deep Learning Model for Genomic Prediction of Count Data.

G3 (Bethesda, Md.)
The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used ...

Characterizing chromatin folding coordinate and landscape with deep learning.

PLoS computational biology
Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less u...

A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes.

Cancer research
Our understanding of noncoding mutations in cancer genomes has been derived primarily from mutational recurrence analysis by aggregating clinical samples on a large scale. These cohort-based approaches cannot directly identify individual pathogenic n...

Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information.

PloS one
With advances in sequencing technology, a vast amount of genomic sequence information has become available. However, annotating biological functions particularly of non-protein-coding regions in genome sequences without experiments is still a challen...

A supervised learning framework for chromatin loop detection in genome-wide contact maps.

Nature communications
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on ...

Enhancer recognition and prediction during spermatogenesis based on deep convolutional neural networks.

Molecular omics
MOTIVATION: enhancers play an important role in the regulation of gene expression during spermatogenesis. The development of ChIP-Chip and ChIP-Seq sequencing technology has enabled researchers to focus on the relationship between enhancers and DNA s...

Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.

Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie
The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore c...

SpliceFinder: ab initio prediction of splice sites using convolutional neural network.

BMC bioinformatics
BACKGROUND: Identifying splice sites is a necessary step to analyze the location and structure of genes. Two dinucleotides, GT and AG, are highly frequent on splice sites, and many other patterns are also on splice sites with important biological fun...