AIMC Topic: Eukaryota
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Tiara: deep learning-based classification system for eukaryotic sequences.
Bioinformatics (Oxford, England)
Jan 3, 2022
MOTIVATION: With a large number of metagenomic datasets becoming available, eukaryotic metagenomics emerged as a new challenge. The proper classification of eukaryotic nuclear and organellar genomes is an essential step toward a better understanding ...
SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning.
Briefings in bioinformatics
Sep 2, 2021
MOTIVATION: mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals import...
The Gene Ontology Resource: 20 years and still GOing strong.
Nucleic acids research
Jan 8, 2019
The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under cont...
Learned protein embeddings for machine learning.
Bioinformatics (Oxford, England)
Aug 1, 2018
MOTIVATION: Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enab...
DeepSig: deep learning improves signal peptide detection in proteins.
Bioinformatics (Oxford, England)
May 15, 2018
MOTIVATION: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization.
SANA NetGO: a combinatorial approach to using Gene Ontology (GO) terms to score network alignments.
Bioinformatics (Oxford, England)
Apr 15, 2018
MOTIVATION: Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure GO similarity between proteins in isolation, but proteins in a network alignment are not isolate...
DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.
Bioinformatics (Oxford, England)
Feb 15, 2018
MOTIVATION: A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often ...
Genome-wide pre-miRNA discovery from few labeled examples.
Bioinformatics (Oxford, England)
Feb 15, 2018
MOTIVATION: Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative ex...
DeepLoc: prediction of protein subcellular localization using deep learning.
Bioinformatics (Oxford, England)
Nov 1, 2017
MOTIVATION: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of...
Machine learning methods and systems for data-driven discovery in biomedical informatics.
Methods (San Diego, Calif.)
Oct 1, 2017
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