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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 OpenAlexvenueno aff
S. Sarath Raj, M. Ranjith, A. Ramesh

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.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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

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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2017
Admission routes1
Has abstractyes

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