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KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction.

Briefings in bioinformatics
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...

Identification of Taihang-chicken-specific genetic markers using genome-wide SNPs and machine learning: BREED-SPECIFIC SNPS OF TAIHANG CHICKEN.

Poultry science
Taihang is an indigenous breed in Hebei Province and has a long history of evolution. To uncover the genetic basis and protect the genetic resources, it is important to develop accurate markers to identify Taihang at the molecular level. In this stud...

An investigation of machine learning methods applied to genomic prediction in yellow-feathered broilers.

Poultry science
Machine learning (ML) methods have rapidly developed in various theoretical and practical research areas, including predicting genomic breeding values for large livestock animals. However, few studies have investigated the application of ML in broile...

Sequence-Based Machine Learning Reveals 3D Genome Differences between Bonobos and Chimpanzees.

Genome biology and evolution
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...

LEC-Codec: Learning-Based Genome Data Compression.

IEEE/ACM transactions on computational biology and bioinformatics
In this paper, we propose a Learning-based gEnome Codec (LEC), which is designed for high efficiency and enhanced flexibility. The LEC integrates several advanced technologies, including Group of Bases (GoB) compression, multi-stride coding and bidir...

Recipes and ingredients for deep learning models of 3D genome folding.

Current opinion in genetics & development
Three-dimensional genome folding plays roles in gene regulation and disease. In this review, we compare and contrast recent deep learning models for predicting genome contact maps. We survey preprocessing, architecture, training, evaluation, and inte...

Fast and accurate deep learning scans for signatures of natural selection in genomes using FASTER-NN.

Communications biology
Deep learning classification models based on Convolutional Neural Networks (CNNs) are increasingly used in population genetic inference for detecting signatures of natural selection. Prevailing detection methods treat the design of the classifier as ...

Genomic language models: opportunities and challenges.

Trends in genetics : TIG
Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences. Just as the goal of natural language processing is to understand sequences of words, a major objective i...

Machine and Deep Learning Methods for Predicting 3D Genome Organization.

Methods in molecular biology (Clifton, N.J.)
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...

Geometric deep learning framework for de novo genome assembly.

Genome research
The critical stage of every de novo genome assembler is identifying paths in assembly graphs that correspond to the reconstructed genomic sequences. The existing algorithmic methods struggle with this, primarily due to repetitive regions causing comp...