AIMC Topic: Eukaryota
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NetStart 2.0: prediction of eukaryotic translation initiation sites using a protein language model.
BMC bioinformatics
Aug 19, 2025
BACKGROUND: Accurate identification of translation initiation sites is essential for the proper translation of mRNA into functional proteins. In eukaryotes, the choice of the translation initiation site is influenced by multiple factors, including it...
Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder.
Nature communications
May 4, 2025
Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protei...
Exploring species taxonomic kingdom using information entropy and nucleotide compositional features of coding sequences based on machine learning methods.
Methods (San Diego, Calif.)
Apr 23, 2025
The flow of genetic information from DNA to protein is governed by the central dogma of molecular biology. Genetic drift and mutations usually lead to changes in DNA composition, thereby affecting the coding sequences (CDS) that encode functional pro...
Probing the eukaryotic microbes of ruminants with a deep-learning classifier and comprehensive protein databases.
Genome research
Feb 14, 2025
Metagenomics, particularly genome-resolved metagenomics, have significantly deepened our understanding of microbes, illuminating their taxonomic and functional diversity and roles in ecology, physiology, and evolution. However, eukaryotic populations...
CNN-BLSTM based deep learning framework for eukaryotic kinome classification: An explainability based approach.
Computational biology and chemistry
Aug 8, 2024
Classification of protein families from their sequences is an enduring task in Proteomics and related studies. Numerous deep-learning models have been moulded to tackle this challenge, but due to the black-box character, they still fall short in reli...
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. ...
An atlas of protein homo-oligomerization across domains of life.
Cell
Feb 6, 2024
Protein structures are essential to understanding cellular processes in molecular detail. While advances in artificial intelligence revealed the tertiary structure of proteins at scale, their quaternary structure remains mostly unknown. We devise a s...
Machine learning-aided scoring of synthesis difficulties for designer chromosomes.
Science China. Life sciences
Mar 3, 2023
Designer chromosomes are artificially synthesized chromosomes. Nowadays, these chromosomes have numerous applications ranging from medical research to the development of biofuels. However, some chromosome fragments can interfere with the chemical syn...
DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning.
Molecules (Basel, Switzerland)
Mar 1, 2023
The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive,...
AI reveals structures of protein complexes.
Science (New York, N.Y.)
Nov 11, 2021
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