AIMC Topic: Genome
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Machine learning identification of enhancers in the rhesus macaque genome.
Neuron
May 21, 2025
Nonhuman primate (NHP) neuroanatomy and cognitive complexity make NHPs ideal models to study human neurobiology and disease. However, NHP circuit-function investigations are limited by the availability of molecular reagents that are effective in NHPs...
Unraveling the three-dimensional genome structure using machine learning.
BMB reports
May 1, 2025
The study of chromatin interactions has advanced considerably with technologies such as high-throughput chromosome conformation capture (Hi-C) sequencing, providing a genome-wide view of physical interactions within the nucleus. These techniques have...
Machine and Deep Learning Methods for Predicting 3D Genome Organization.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2025
Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expressi...
KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.
Briefings in bioinformatics
Nov 22, 2024
Nonadditive genetic effects pose significant challenges to traditional genomic selection methods for quantitative traits. Machine learning approaches, particularly kernel-based methods, offer promising solutions to overcome these limitations. In this...
Sequence-Based Machine Learning Reveals 3D Genome Differences between Bonobos and Chimpanzees.
Genome biology and evolution
Nov 1, 2024
The 3D structure of the genome is an important mediator of gene expression. As phenotypic divergence is largely driven by gene regulatory variation, comparing genome 3D contacts across species can further understanding of the molecular basis of speci...
Classification accuracy of machine learning algorithms for Chinese local cattle breeds using genomic markers.
Yi chuan = Hereditas
Jul 1, 2024
Accurate breed classification is required for the conservation and utilization of farm animal genetic resources. Traditional classification methods mainly rely on phenotypic characterization. However, it is difficult to distinguish between the highly...
Coding genomes with gapped pattern graph convolutional network.
Bioinformatics (Oxford, England)
Mar 29, 2024
MOTIVATION: Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets. Howe...
From tradition to innovation: conventional and deep learning frameworks in genome annotation.
Briefings in bioinformatics
Mar 27, 2024
Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the...
EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.
Bioinformatics (Oxford, England)
Mar 4, 2024
SUMMARY: Deep neural networks (DNNs) have been widely applied to predict the molecular functions of the non-coding genome. DNNs are data hungry and thus require many training examples to fit data well. However, functional genomics experiments typical...
Evidential deep learning for trustworthy prediction of enzyme commission number.
Briefings in bioinformatics
Nov 22, 2023
The rapid growth of uncharacterized enzymes and their functional diversity urge accurate and trustworthy computational functional annotation tools. However, current state-of-the-art models lack trustworthiness on the prediction of the multilabel clas...
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