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A deep learning approach to automate whole-genome prediction of diverse epigenomic modifications in plants.

The New phytologist
Epigenetic modifications function in gene transcription, RNA metabolism, and other biological processes. However, multiple factors currently limit the scientific utility of epigenomic datasets generated for plants. Here, using deep-learning approache...

DeepG4: A deep learning approach to predict cell-type specific active G-quadruplex regions.

PLoS computational biology
DNA is a complex molecule carrying the instructions an organism needs to develop, live and reproduce. In 1953, Watson and Crick discovered that DNA is composed of two chains forming a double-helix. Later on, other structures of DNA were discovered an...

Machine learning-based identification and characterization of 15 novel pathogenic SUOX missense mutations.

Molecular genetics and metabolism
Isolated sulfite oxidase deficiency (ISOD) is a rare hereditary metabolic disease caused by absence of functional sulfite oxidase (SO) due to mutations of the SUOX gene. SO oxidizes toxic sulfite and sulfite accumulation is associated with neurologic...

Application of a Poisson deep neural network model for the prediction of count data in genome-based prediction.

The plant genome
Genomic selection (GS) is revolutionizing conventional ways of developing new plants and animals. However, because it is a predictive methodology, GS strongly depends on statistical and machine learning to perform these predictions. For continuous ou...

Deep-learning power and perspectives for genomic selection.

The plant genome
Deep learning (DL) is revolutionizing the development of artificial intelligence systems. For example, before 2015, humans were better than artificial machines at classifying images and solving many problems of computer vision (related to object loca...

Machine learning for profile prediction in genomics.

Current opinion in chemical biology
A recent deluge of publicly available multi-omics data has fueled the development of machine learning methods aimed at investigating important questions in genomics. Although the motivations for these methods vary, a task that is commonly adopted is ...

i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning.

BioMed research international
As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) plays a crucial role in controlling gene replication, expression, cell cycle, DNA replication, and differentiation. The accurate identification of 4mC sites is necessary to und...

HARVESTMAN: a framework for hierarchical feature learning and selection from whole genome sequencing data.

BMC bioinformatics
BACKGROUND: Supervised learning from high-throughput sequencing data presents many challenges. For one, the curse of dimensionality often leads to overfitting as well as issues with scalability. This can bring about inaccurate models or those that re...

Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests.

Molecular ecology resources
Gradient Forests (GF) is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we (i) provide the ...