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Sugirthan Sivalingam

dblp:394/4465 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0001-5239-5137ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

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 · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › statistical genetics
phenotype prediction
0.612022
GenRisk: a tool for comprehensive genetic risk modeling · Bioinform. 2022
Bioinformatics and computational biology › statistical genetics › genomic prediction
polygenic risk score
0.612022
GenRisk: a tool for comprehensive genetic risk modeling · Bioinform. 2022

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

regression · 0.6classification · 0.6
YearPublicationVenuePosition
2022 GenRisk: a tool for comprehensive genetic risk modeling
abstract
SUMMARY: The genetic architecture of complex traits can be influenced by both many common regulatory variants with small effect sizes and rare deleterious variants in coding regions with larger effect sizes. However, the two kinds of genetic contributions are typically analyzed independently. Here, we present GenRisk, a python package for the computation and the integration of gene scores based on the burden of rare deleterious variants and common-variants-based polygenic risk scores. The derived scores can be analyzed within GenRisk to perform association tests or to derive phenotype prediction models by testing multiple classification and regression approaches. GenRisk is compatible with VCF input file formats. AVAILABILITY AND IMPLEMENTATION: GenRisk is an open source publicly available python package that can be downloaded or installed from Github (https://github.com/AldisiRana/GenRisk). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Rana Aldisi, Emadeldin Hassanin, Sugirthan Sivalingam, Andreas Buness, Hannah Klinkhammer, Andreas Mayr 0001, Holger Fröhlich, Peter M. Krawitz, Carlo Maj
Bioinform.3