AIMC Topic: Genome

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Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models.

Molecular omics May 7, 2024
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequenci...
Animals Metabolic Networks and Pathways Humans Machine Learning Genome Translational Science, Biomedical Models, Biological Translational Research, Biomedical
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DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes.

Scientific reports Apr 21, 2024
Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DLMs have been used to predict protein structures, transcription factor-binding sites, and promoters. ...
Neural Networks, Computer Deep Learning Binding Sites Eukaryota Computational Biology Transcription Factors Eukaryotic Cells Genome Prokaryotic Cells
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KEGG orthology prediction of bacterial proteins using natural language processing.

BMC bioinformatics Apr 11, 2024
BACKGROUND: The advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by ...
Bacterial Proteins Amino Acid Sequence Natural Language Processing Molecular Sequence Annotation Genome
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Accurate top protein variant discovery via low-N pick-and-validate machine learning.

Cell systems Feb 9, 2024
A strategy to obtain the greatest number of best-performing variants with least amount of experimental effort over the vast combinatorial mutational landscape would have enormous utility in boosting resource producibility for protein engineering. Tow...
Mutation Protein Engineering Genome Humans Machine Learning
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Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data.

BMC genomics Feb 7, 2024
BACKGROUND: The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore r...
Genome Plant Breeding Deep Learning Genomics Animals Machine Learning
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Multi-label classification with XGBoost for metabolic pathway prediction.

BMC bioinformatics Feb 1, 2024
BACKGROUND: Metabolic pathway prediction is one possible approach to address the problem in system biology of reconstructing an organism's metabolic network from its genome sequence. Recently there have been developments in machine learning-based pat...
Biology Genome Machine Learning Metabolic Networks and Pathways
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Personal transcriptome variation is poorly explained by current genomic deep learning models.

Nature genetics Nov 30, 2023
Genomic deep learning models can predict genome-wide epigenetic features and gene expression levels directly from DNA sequence. While current models perform well at predicting gene expression levels across genes in different cell types from the refer...
Genomics Transcriptome Humans Genome Genetic Variation Deep Learning
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Functional annotation of enzyme-encoding genes using deep learning with transformer layers.

Nature communications Nov 14, 2023
Functional annotation of open reading frames in microbial genomes remains substantially incomplete. Enzymes constitute the most prevalent functional gene class in microbial genomes and can be described by their specific catalytic functions using the ...
Escherichia coli Proteins Molecular Sequence Annotation Genome Escherichia coli K12 Deep Learning Open Reading Frames
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disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data.

BMC bioinformatics Oct 11, 2023
Spatial genetic variation is shaped in part by an organism's dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data. Our neural network performs ...
Genome Polymorphism, Single Nucleotide Software Humans Genomics Neural Networks, Computer
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Harnessing deep learning for population genetic inference.

Nature reviews. Genetics Sep 4, 2023
In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of p...
Biological Evolution Deep Learning Genomics Genetics, Population Genome
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