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Record W1968431759 · doi:10.1504/ijbra.2013.056620

Challenges in the miRNA research

2013· review· en· W1968431759 on OpenAlex
Tiratha Raj Singh, Arun Gupta, Prashanth Suravajhala

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Bioinformatics Research and Applications · 2013
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsmicroRNABiologyComputational biologyGeneAnnotationGeneticsBioinformatics

Abstract

fetched live from OpenAlex

While it is known that the human genes are regulated by microRNAs (miRNAs), recent links with cancer and other diseases have widely caught interest. With several bioinformatics platforms and approaches on rise that has led to discovery of human miRNAs, validation and need for understanding miRNAs from their progenitor messenger RNAs (mRNAs) have arisen. Furthermore, the miRNAs are known to have synergism involving regulation of their condition-specific target genes (mRNAs). In this review, we provide a bioinformatics approach of the miRNAs and their challenges with respect to annotation. With introduction of sequence-specific miRNA signatures recently found, we discussed myriad of dimensions where miRNAs are being associated with several putative functional and evolutionary events, and then we asked a question how far and relevant is the association of miRNAs with mRNAs?

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.992
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.285
GPT teacher head0.496
Teacher spread0.212 · 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