AIMC Topic: Genome
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What does evolution make? Learning in living lineages and machines.
Trends in genetics : TIG
Jun 10, 2025
How does genomic information unfold, to give rise to self-constructing living organisms with problem-solving capacities at all levels of organization? We review recent progress that unifies work in developmental genetics and machine learning (ML) to ...
Geometric deep learning framework for de novo genome assembly.
Genome research
Apr 14, 2025
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...
Recipes and ingredients for deep learning models of 3D genome folding.
Current opinion in genetics & development
Jan 24, 2025
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
Jan 15, 2025
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
Jan 2, 2025
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...
LEC-Codec: Learning-Based Genome Data Compression.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 10, 2024
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...
Identification of Taihang-chicken-specific genetic markers using genome-wide SNPs and machine learning: BREED-SPECIFIC SNPS OF TAIHANG CHICKEN.
Poultry science
Nov 22, 2024
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
Nov 1, 2024
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...
Encoding innate ability through a genomic bottleneck.
Proceedings of the National Academy of Sciences of the United States of America
Sep 12, 2024
Animals are born with extensive innate behavioral capabilities, which arise from neural circuits encoded in the genome. However, the information capacity of the genome is orders of magnitude smaller than that needed to specify the connectivity of an ...
Machine learning for the advancement of genome-scale metabolic modeling.
Biotechnology advances
Jun 27, 2024
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies...
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