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SVision: a deep learning approach to resolve complex structural variants.

Nature methods
Complex structural variants (CSVs) encompass multiple breakpoints and are often missed or misinterpreted. We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and haracterize CSVs from long-read sequ...

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.

Molecular plant
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by inco...

Deciphering signatures of natural selection via deep learning.

Briefings in bioinformatics
Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. However, it remains challenging to detect loci under complex spatially varying selection. We propose a deep learning...

Quartet Based Gene Tree Imputation Using Deep Learning Improves Phylogenomic Analyses Despite Missing Data.

Journal of computational biology : a journal of computational molecular cell biology
Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, for a combination of reasons (ranging from sampling biases to more biological causes, as in gene birth and loss), gene...

DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

PLoS computational biology
In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide conta...

Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data.

Genes
The high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of markers capable of increasing the accuracy of predictions compared to the current commercial single...

Machine learning applications for transcription level and phenotype predictions.

IUBMB life
Predicting phenotypes and complex traits from genomic variations has always been a big challenge in molecular biology, at least in part because the task is often complicated by the influences of external stimuli and the environment on regulation of g...

Machine learning for predicting phenotype from genotype and environment.

Current opinion in biotechnology
Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the pro...

Defining the extent of gene function using ROC curvature.

Bioinformatics (Oxford, England)
MOTIVATION: Interactions between proteins help us understand how genes are functionally related and how they contribute to phenotypes. Experiments provide imperfect 'ground truth' information about a small subset of potential interactions in a specif...

EMBL's European Bioinformatics Institute (EMBL-EBI) in 2022.

Nucleic acids research
The European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the Wellcome Genome Campus in Hinxton, UK, EMBL-EBI is one of six sites of the Europe...