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Record W2979340817 · doi:10.1111/tra.12704

Biomolecular condensates in neurodegeneration and cancer

2019· review· en· W2979340817 on OpenAlexafffund
Stephanie Spannl, Maria Tereshchenko, Giovanni J. Mastromarco, Sean J. Ihn, Hyun O. Lee

Bibliographic record

VenueTraffic · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence FundConnaught Fund
KeywordsNeurodegenerationBiologyOrganelleIntracellularCompartment (ship)Cell biologyNeuroscienceCell physiologyComputational biologyDiseaseCellBiochemistry

Abstract

fetched live from OpenAlex

The intracellular environment is partitioned into functionally distinct compartments containing specific sets of molecules and reactions. Biomolecular condensates, also referred to as membrane-less organelles, are diverse and abundant cellular compartments that lack membranous enclosures. Molecules assemble into condensates by phase separation; multivalent weak interactions drive molecules to separate from their surroundings and concentrate in discrete locations. Biomolecular condensates exist in all eukaryotes and in some prokaryotes, and participate in various essential house-keeping, stress-response and cell type-specific processes. An increasing number of recent studies link abnormal condensate formation, composition and material properties to a number of disease states. In this review, we discuss current knowledge and models describing the regulation of condensates and how they become dysregulated in neurodegeneration and cancer. Further research on the regulation of biomolecular phase separation will help us to better understand their role in cell physiology and disease.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.046
GPT teacher head0.356
Teacher spread0.310 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations126
Published2019
Admission routes2
Has abstractyes

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