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Record W2562077745 · doi:10.1016/j.cpb.2016.12.005

Gramene database: Navigating plant comparative genomics resources

2016· article· en· W2562077745 on OpenAlexaff
Parul Gupta, Sushma Naithani, Marcela K Tello-Ruiz, Kapeel Chougule, Peter D’Eustachio, Antonio Fabregat, Yinping Jiao, Maria Keays, Young Koung Lee, Sunita Kumari, Joseph Mulvaney, Andrew Olson, Justin Preece, Joshua C. Stein, Sharon Wei, Joel Weiser, Laura Huerta, Robert Petryszak, Paul Kersey, Lincoln Stein, Doreen Ware, Pankaj Jaiswal

Bibliographic record

VenueCurrent Plant Biology · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsOntario Institute for Cancer Research
FundersNational Institute of General Medical SciencesNational Human Genome Research InstituteGuangxi Academy of Sciences
KeywordsGenomicsAnnotationComparative genomicsBiologyGenomeExploitComputational biologyResource (disambiguation)Phylogenetic treeBiological databaseData scienceComputer scienceBioinformaticsGeneticsGene

Abstract

fetched live from OpenAlex

Gramene (http://www.gramene.org) is an online, open source, curated resource for plant comparative genomics and pathway analysis designed to support researchers working in plant genomics, breeding, evolutionary biology, system biology, and metabolic engineering. It exploits phylogenetic relationships to enrich the annotation of genomic data and provides tools to perform powerful comparative analyses across a wide spectrum of plant species. It consists of an integrated portal for querying, visualizing and analyzing data for 44 plant reference genomes, genetic variation data sets for 12 species, expression data for 16 species, curated rice pathways and orthology-based pathway projections for 66 plant species including various crops. Here we briefly describe the functions and uses of the Gramene database.

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)0.002
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.007
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0040.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0260.017

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 head0.048
GPT teacher head0.305
Teacher spread0.257 · 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
GenreSoftware

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

Citations58
Published2016
Admission routes1
Has abstractyes

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