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Record W2106415248 · doi:10.1002/mas.20294

Lipidomics era: Accomplishments and challenges

2010· review· en· W2106415248 on OpenAlexafffund
Maroun Bou Khalil, Weimin Hou, Hu Zhou, Fred Elisma, Leigh Anne Swayne, Alexandre P. Blanchard, Zemin Yao, Steffany A. L. Bennett, Daniel Figeys

Bibliographic record

VenueMass Spectrometry Reviews · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsLipidomicsChemistryLipid metabolismSignal transductionComputational biologyBiochemistryCell biologyBiology

Abstract

fetched live from OpenAlex

Lipid mediators participate in signal transduction pathways, proliferation, apoptosis, and membrane trafficking in the cell. Lipids are highly complex and diverse owing to the various combinations of polar headgroups, fatty acyl chains, and backbone structures. This structural diversity continues to pose a challenge for lipid analysis. Here we review the current state of the art in lipidomics research and discuss the challenges facing this field. The latest technological developments in mass spectrometry, the role of bioinformatics, and the applications of lipidomics in lipid metabolism and cellular physiology and pathology are also discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0010.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.071
GPT teacher head0.323
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations195
Published2010
Admission routes2
Has abstractyes

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