Volume 9 Supplement 1
Edited by Timothy R Hughes and Frederick P Roth
Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help...
The wide availability of genome-scale data for several organisms has stimulated interest in computational approaches to gene function prediction. Diverse machine learning methods have been applied to unicellul...
Most successful computational approaches for protein function prediction integrate multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. The most accurate of ...
The complete set of mouse genes, as with the set of human genes, is still largely uncharacterized, with many pieces of experimental evidence accumulating regarding the activities and expression of the genes, b...
In predicting hierarchical protein function annotations, such as terms in the Gene Ontology (GO), the simplest approach makes predictions for each term independently. However, this approach has the unfortunate...
Learning the function of genes is a major goal of computational genomics. Methods for inferring gene function have typically fallen into two categories: 'guilt-by-profiling', which exploits correlation between...
Individual researchers are struggling to keep up with the accelerating emergence of high-throughput biological data, and to extract information that relates to their specific questions. Integration of accumula...
2022 Citation Impact
12.3 - 2-year Impact Factor
17.4 - 5-year Impact Factor
3.476 - SNIP (Source Normalized Impact per Paper)
9.249 - SJR (SCImago Journal Rank)
2023 Speed
21 days submission to first editorial decision for all manuscripts (Median)
277 days submission to accept (Median)
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6,688,476 downloads
12,515 Altmetric mentions
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