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Record W2398477216 · doi:10.1007/978-1-62703-429-6_4

Identifying Myc Interactors

2013· article· en· W2398477216 on OpenAlexafffund
Romina Ponzielli, William B. Tu, Igor Jurišica, Linda Z. Penn

Bibliographic record

VenueMethods in molecular biology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstituteUniversity of TorontoOntario Institute for Cancer Research
FundersCanadian Institutes of Health Research
KeywordsIn silicoImmunoprecipitationProtein–protein interactionIn vitroComputational biologyCytoplasmIdentification (biology)ChemistryCell biologyBiologyBiochemistryGene

Abstract

fetched live from OpenAlex

In this chapter, we discuss in detail two essential methods used to evaluate the interaction of Myc with another protein of interest: co-immunoprecipitation (Co-IP) and in vitro pull-down assays. Co-IP is a method that, by immunoaffinity, allows the identification of protein-protein interactions within cells. We provide methods to conduct Co-IPs from whole-cell extracts as well as cytoplasmic and nuclear-enriched fractions. By contrast, the pull-down assay evaluates whether a bait protein that is bound to a solid support can specifically interact with a prey protein that is in solution. We provide methods to conduct in vitro pull-downs and further detail how to use this assay to distinguish whether a protein-protein interaction is direct or indirect. We also discuss methods used to screen for Myc interactors and provide an in silico strategy to help prioritize hits for further validation using the described Co-IP and in vitro pull-down assays.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.004

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 head0.022
GPT teacher head0.366
Teacher spread0.344 · 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 designBench or experimental
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

Citations3
Published2013
Admission routes2
Has abstractyes

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