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Ines P. Silva

dblp:241/2008 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 67% Computational science and engineering · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational science and engineering
bias correction
0.412019
bcGST - an interactive bias-correction method to identify over-represented gene-sets in boutique arrays · Bioinform. 2019
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
gene set enrichment analysis
0.412019
bcGST - an interactive bias-correction method to identify over-represented gene-sets in boutique arrays · Bioinform. 2019
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
over-representation analysis
0.412019
bcGST - an interactive bias-correction method to identify over-represented gene-sets in boutique arrays · Bioinform. 2019

Methods — techniques the papers use, named apart from their topics

fisher's exact test · 0.4
YearPublicationVenuePosition
2019 bcGST - an interactive bias-correction method to identify over-represented gene-sets in boutique arrays
abstract
MOTIVATION: Gene annotation and pathway databases such as Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes are important tools in Gene-Set Test (GST) that describe gene biological functions and associated pathways. GST aims to establish an association relationship between a gene-set of interest and an annotation. Importantly, GST tests for over-representation of genes in an annotation term. One implicit assumption of GST is that the gene expression platform captures the complete or a very large proportion of the genome. However, this assumption is neither satisfied for the increasingly popular boutique array nor the custom designed gene expression profiling platform. Specifically, conventional GST is no longer appropriate due to the gene-set selection bias induced during the construction of these platforms. RESULTS: We propose bcGST, a bias-corrected GST by introducing bias-correction terms in the contingency table needed for calculating the Fisher's Exact Test. The adjustment method works by estimating the proportion of genes captured on the array with respect to the genome in order to assist filtration of annotation terms that would otherwise be falsely included or excluded. We illustrate the practicality of bcGST and its stability through multiple differential gene expression analyses in melanoma and the Cancer Genome Atlas cancer studies. AVAILABILITY AND IMPLEMENTATION: The bcGST method is made available as a Shiny web application at http://shiny.maths.usyd.edu.au/bcGST/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Kevin Y. X. Wang, Alexander M. Menzies, Ines P. Silva, James S. Wilmott, Yibing Yan, Matthew Wongchenko, Richard F. Kefford, Richard A. Scolyer, Georgina V. Long, Garth Tarr, Samuel Müller 0001, Jean Y. H. Yang
Bioinform.3