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Record W2774022032 · doi:10.24870/cjb.2017-a279

Computational Profiling of Deleterious Non-Synonymous SNP’s in HFE

2017· article· en· W2774022032 on OpenAlex
S. Sarath Raj, M. Ranjith, A. Ramesh

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Biotechnology · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsComputational biologySNPBiologyGeneticsProfiling (computer programming)Evolutionary biologyComputer scienceSingle-nucleotide polymorphismGeneGenotype

Abstract

fetched live from OpenAlex

Liver cirrhosis describes a condition where scar tissue gradually replaces healthy cells in liver. The main causes are sustained, excessive alcohol consumption, viral hepatitis B and C, and fatty liver disease -however, there are other possible causes. Hemochromatosis is most common form of iron overload disease. Three types of hemochromatosis are primary hemochromatosis, also called hereditary hemochromatosis; secondary hemochromatosis; and neonatal hemochromatosis. The HFE gene helps regulate the amount of iron absorbed from food and inherited genetic defects or mutation in HFE [C282Y] cause primary hemochromatosis. Computational approach is sought to determine other similar mutations in this gene. In-silico tools such as SIFT, Polyphen 2.0, and PROVEAN were employed to determine the various deleterious ns-SNPs of HFE that may influence cystic fibrosis.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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