AIMC Topic: Molecular Sequence Annotation
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Machine learning approaches to predict lupus disease activity from gene expression data.
Scientific reports
Jul 3, 2019
The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different mi...
Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.
BMC bioinformatics
Jun 17, 2019
BACKGROUND: Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in protein...
HOME: a histogram based machine learning approach for effective identification of differentially methylated regions.
BMC bioinformatics
May 16, 2019
BACKGROUND: The development of whole genome bisulfite sequencing has made it possible to identify methylation differences at single base resolution throughout an entire genome. However, a persistent challenge in DNA methylome analysis is the accurate...
GO functional similarity clustering depends on similarity measure, clustering method, and annotation completeness.
BMC bioinformatics
Mar 27, 2019
BACKGROUND: Biological knowledge, and therefore Gene Ontology annotation sets, for human genes is incomplete. Recent studies have reported that biases in available GO annotations result in biased estimates of functional similarities of genes, but it ...
FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments.
BMC bioinformatics
Feb 15, 2019
BACKGROUND: Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molec...
Annotation of gene product function from high-throughput studies using the Gene Ontology.
Database : the journal of biological databases and curation
Jan 1, 2019
High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The repres...
miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase.
BMC bioinformatics
Dec 31, 2018
BACKGROUND: miRBase is the primary repository for published miRNA sequence and annotation data, and serves as the "go-to" place for miRNA research. However, the definition and annotation of miRNAs have been changed significantly across different vers...
Identifying mouse developmental essential genes using machine learning.
Disease models & mechanisms
Dec 13, 2018
The genes that are required for organismal survival are annotated as 'essential genes'. Identifying all the essential genes of an animal species can reveal critical functions that are needed during the development of the organism. To inform studies o...
Automatic gene annotation using GO terms from cellular component domain.
BMC medical informatics and decision making
Dec 7, 2018
BACKGROUND: The Gene Ontology (GO) is a resource that supplies information about gene product function using ontologies to represent biological knowledge. These ontologies cover three domains: Cellular Component (CC), Molecular Function (MF), and Bio...
Porcine single nucleotide polymorphisms and their functional effect: an update.
BMC research notes
Dec 4, 2018
OBJECTIVE: To aid in the development of a comprehensive list of functional variants in the swine genome, single nucleotide polymorphisms (SNP) were identified from whole genome sequence of 240 pigs. Interim data from 72 animals in this study was publ...
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