AIMC Topic: Eukaryota
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Hereditary disease prediction in eukaryotic DNA: an adaptive signal processing approach.
Nucleosides, nucleotides & nucleic acids
Jun 22, 2020
Hereditary disease prediction in eukaryotic DNA using signal processing approaches is an incredible work in bioinformatics. Researchers of various fields are trying to put forth a noninvasive approach to forecast the disease-related genes. As disease...
ProNA2020 predicts protein-DNA, protein-RNA, and protein-protein binding proteins and residues from sequence.
Journal of molecular biology
Mar 4, 2020
The intricate details of how proteins bind to proteins, DNA, and RNA are crucial for the understanding of almost all biological processes. Disease-causing sequence variants often affect binding residues. Here, we described a new, comprehensive system...
Machine learning for discovering missing or wrong protein function annotations : A comparison using updated benchmark datasets.
BMC bioinformatics
Sep 23, 2019
BACKGROUND: A massive amount of proteomic data is generated on a daily basis, nonetheless annotating all sequences is costly and often unfeasible. As a countermeasure, machine learning methods have been used to automatically annotate new protein func...
Designing Eukaryotic Gene Expression Regulation Using Machine Learning.
Trends in biotechnology
Aug 17, 2019
Controlling the expression of genes is one of the key challenges of synthetic biology. Until recently fine-tuned control has been out of reach, particularly in eukaryotes owing to their complexity of gene regulation. With advances in machine learning...
SignalP 5.0 improves signal peptide predictions using deep neural networks.
Nature biotechnology
Feb 18, 2019
Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish ...
Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring.
Molecular ecology resources
Aug 3, 2018
Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) could overcome many limitations ...
Monitoring changes in the Gene Ontology and their impact on genomic data analysis.
GigaScience
Aug 1, 2018
BACKGROUND: The Gene Ontology (GO) is one of the most widely used resources in molecular and cellular biology, largely through the use of "enrichment analysis." To facilitate informed use of GO, we present GOtrack (https://gotrack.msl.ubc.ca), which ...
DeepText2GO: Improving large-scale protein function prediction with deep semantic text representation.
Methods (San Diego, Calif.)
Jun 6, 2018
As of April 2018, UniProtKB has collected more than 115 million protein sequences. Less than 0.15% of these proteins, however, have been associated with experimental GO annotations. As such, the use of automatic protein function prediction (AFP) to r...
Improved ontology for eukaryotic single-exon coding sequences in biological databases.
Database : the journal of biological databases and curation
Jan 1, 2018
Efficient extraction of knowledge from biological data requires the development of structured vocabularies to unambiguously define biological terms. This paper proposes descriptions and definitions to disambiguate the term 'single-exon gene'. Eukaryo...
SnoReport 2.0: new features and a refined Support Vector Machine to improve snoRNA identification.
BMC bioinformatics
Dec 15, 2016
BACKGROUND: snoReport uses RNA secondary structure prediction combined with machine learning as the basis to identify the two main classes of small nucleolar RNAs, the box H/ACA snoRNAs and the box C/D snoRNAs. Here, we present snoReport 2.0, which s...
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