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Record W2418599318 · doi:10.1385/1-59259-356-9:99

Kinetworks<sup>TM</sup> Protein Kinase Multiblot Analysis

2003· article· en· W2418599318 on OpenAlex
Steven Pelech, Catherine Sutter, Hong Zhang

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

VenueHumana Press eBooks · 2003
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsKinexus Bioinformatics Corporation (Canada)University of British Columbia
Fundersnot available
KeywordsKinasePhosphorylationSignal transductionGel electrophoresisChemistryProteomicsBiochemistryMolecular biologyProtein phosphorylationProtein kinase CBiologyProtein kinase AGene

Abstract

fetched live from OpenAlex

The proteomics analysis of protein kinases and other cell-signaling proteins in tumor samples by traditional two-dimensional (2-D) gel electrophoresis is complicated by the low abundance of these regulatory proteins relative to metabolic enzymes and structural proteins. We present an antibody-based method called Kinetworks that relies on sodium dodecyl sulfate (SDS)-poly-acrylamide minigel electrophoresis and multilane immunoblotters to permit the specific and quantitative detection of 45 or more protein kinases or other signal transduction proteins at once. The technique can also permit the resolution of these proteins based on differences in their phosphorylation state and other forms of covalent modification. Kinetworks profiling of protein kinases in solid human tumors and cell lines can reveal profound differences in their expression and phosphorylation states, which can serve for the identification of cancer diagnostic markers and therapeutic targets for drug discovery.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.029
GPT teacher head0.276
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