MétaCan
Menu
Back to cohort
Record W2020006137 · doi:10.1517/17460440902926399

Potential directions for drug development against galectin-7 in cancer

2009· article· en· W2020006137 on OpenAlexaff
Yves St‐Pierre, Katherine Biron-Pain, Carole G. Campion, Geneviève Lavoie, Frédéric Bouchard, Julie Couillard

Bibliographic record

VenueExpert Opinion on Drug Discovery · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsInstitut National de la Recherche Scientifique
FundersUniversity of Sydney
KeywordsGalectinCancerGalectin-3BiologyCytoplasmCancer cellComputational biologyCell biologyImmunologyGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Galectins are a family of proteins defined by having at least one characteristic carbohydrate recognition domain (CRD) with an affinity for beta-galactosides. Over the recent years, with a better understanding of their role in normal and pathological conditions, they have emerged as promising diagnostic and therapeutic targets in cancer. Whereas most of these studies have focused on galectin-1 and galectin-3, very little attention has been paid to galectin-7, a member of the family that has recently been associated with various forms of cancer. OBJECTIVE: We review the role of galectin-7 in cancer and examine the possible directions that could be exploited to inhibit its role in cancer on the basis of recently identified galectin ligands. CONCLUSION: Although efforts have been made to develop drugs aimed at inhibiting the cancer-promoting propensity of galectins, most of these inhibitors were specific for the CRD region of the molecule and have focused on extracellular functions of galectins. However, galectins may also be involved in protein-protein interactions, most notably in the nucleus. As galectin-7 is expressed in the cytoplasm and the nucleus in cancer cells, it will be important to investigate its nucleocytoplasmic trafficking and how putative drugs will affect its functions in cancer.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.295
Teacher spread0.280 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueExpert Opinion on Drug DiscoverySame topicGalectins and Cancer BiologyFrench-language works237,207