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Record W2911042793 · doi:10.6026/97320630014482

Molecular docking analysis of Cianidanol from Ginkgo biloba with HER2+ breast cancer target

2018· article· en· W2911042793 on OpenAlex

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

VenueBioinformation · 2018
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsEbro Foods (Canada)
Fundersnot available
KeywordsLapatinibGinkgo bilobaTrastuzumab emtansineTrastuzumabMedicineBreast cancerPharmacologyDocking (animal)CancerInternal medicine

Abstract

fetched live from OpenAlex

HER2 is a known therapeutic target for about 30% of breast cancer patients where HER2 is over expressed and this is referred to as HER2 positive breast cancer. This subtype is characterized by a clinical behavior know to be especially aggressive. Improved HER2 targeting agents such as trastuzumab, pertuzumb, lapatinib and ado-trastuzumab emtansine are available. Some patients have shown no response to treatment while others show progress to these agents. Therefore, it is of interest to screen HER2+ with phyto-chemical lead compound from Ginkgo biloba using molecular docking techniques. We screened 25 phyto-chemicals from literature with HER2+. Results show that cianidanol have an acceptable binding energy of (-8.2kcal/mol). Thus, we report the binding properties of cianidanol with HER2+.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.317
Teacher spread0.303 · 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