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Record W2132929353 · doi:10.1080/10942910902927102

Introduction of New Computer Softwares for Classification and Prediction Purposes of Bioactive Peptides: Case Study in Antioxidative Tripeptides

2010· article· en· W2132929353 on OpenAlexafffund
Wasaporn Chanput, S. Nakai, Chockchai Theerakulkait

Bibliographic record

VenueInternational Journal of Food Properties · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Hydrolysis and Bioactive Peptides
Canadian institutionsUniversity of British Columbia
FundersUniversity of British ColumbiaSoutheast Asian Regional Center for Graduate Study and Research in Agriculture
KeywordsTripeptideProlaminBiochemistryChemistryLinoleic acidFood scienceAntioxidantPeptideRice proteinStorage protein

Abstract

fetched live from OpenAlex

Antioxidative tripeptides were successfully classified according to their structure by using Sequence Principal Component Similarity (SPCS) software. During SPCS computation, hydrophobicity was the main characteristic of peptide residues in aspect of antioxidative activity to inhibit lipid peroxidation in linoleic acid model system. The prediction made by Homology Similarity Search (HSS)-BIOPEP combined software indicated that C-hordein fraction from barley protein showed the greatest potential to be antioxidative protein source followed by prolamin fraction from rice protein. Moreover, these homology segments of C-hordein were resistant against the digestion of mixed gastrointestinal enzymes in BIOPEP digestive model system.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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
GenreMethods

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

Citations12
Published2010
Admission routes2
Has abstractyes

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