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Elena Chekmeneva

dblp:309/3865 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · unresolved

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 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › metabolomics
LC-MS data analysis
0.512021
peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets · Bioinform. 2021
Bioinformatics and computational biology
metabolomics
0.512021
peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets · Bioinform. 2021

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

targeted extraction · 0.5peak picking · 0.5annotation · 0.5
YearPublicationVenuePosition
2021 peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets
abstract
SUMMARY: Untargeted liquid chromatography-mass spectrometry (LC-MS) profiling assays are capable of measuring thousands of chemical compounds in a single sample, but unreliable feature extraction and metabolite identification remain considerable barriers to their interpretation and usefulness. peakPantheR (Peak Picking and ANnoTation of High-resolution Experiments in R) is an R package for the targeted extraction and integration of annotated features from LC-MS profiling experiments. It takes advantage of chromatographic and spectral databases and prior information of sample matrix composition to generate annotated and interpretable metabolic phenotypic datasets and power workflows for real-time data quality assessment. AVAILABILITY AND IMPLEMENTATION: peakPantheR is available via Bioconductor (https://bioconductor.org/packages/peakPantheR/). Documentation and worked examples are available at https://phenomecentre.github.io/peakPantheR.github.io/ and https://github.com/phenomecentre/metabotyping-dementia-urine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Arnaud M. Wolfer, Gonçalo D. S. Correia, Caroline J. Sands, Stephane Camuzeaux, Ada H. Y. Yuen, Elena Chekmeneva, Zoltan Takats, Jake T. M. Pearce, Matthew R. Lewis
Bioinform.6