MétaCan
Menu
Back to cohort
Record W2889150696 · doi:10.1016/j.celrep.2018.07.090

Rapid and Integrative Discovery of Retina Regulatory Molecules

2018· article· en· W2889150696 on OpenAlexaff
Nicholas E. Albrecht, Jonathan Alevy, Danye Jiang, Courtney A. Burger, Brian I. Liu, Fenge Li, Julia Wang, Seon‐Young Kim, Chih‐Wei Hsu, Sowmya Kalaga, Uchechukwu Udensi, Chinwe Asomugha, Ritu Bohat, Angelina Gaspero, Monica J. Justice, Peter D. Westenskow, Shinya Yamamoto, John R. Seavitt, Arthur L. Beaudet, Mary E. Dickinson, Melanie A. Samuel

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsHospital for Sick Children
FundersNational Institute of General Medical SciencesNational Eye InstituteNational Human Genome Research InstituteIntellectual and Developmental Disabilities Research CenterBoston Medical CenterNational Institutes of HealthCancer Prevention and Research Institute of TexasEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentRobert and Janice McNair FoundationNational Institute on AgingBrain Research Foundation
KeywordsRetinaRetinalBiologyNeuroscienceGenetic screenGeneComputational biologyMutantGenetics

Abstract

fetched live from OpenAlex

Retinal function relies on precisely organized neurons and synapses and a properly patterned vasculature to support them. Alterations in these features can result in vision loss. However, our understanding of retinal organization pathways remains incomplete because of a lack of methods to rapidly identify neuron and vasculature regulators in mammals. Here we developed a pipeline for the identification of neural and synaptic integrity genes by high-throughput retinal screening (INSiGHT) that analyzes candidate expression, vascular patterning, cellular organization, and synaptic arrangement. Using this system, we examined 102 mutant mouse lines and identified 16 unique retinal regulatory genes. Fifteen of these candidates are identified as novel retina regulators, and many (9 of 16) are associated with human neural diseases. These results expand the genetic landscape involved in retinal circuit organization and provide a road map for continued discovery of mammalian retinal regulators and disease-causing alleles.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.308

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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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 designBench or experimental
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

Citations21
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueCell ReportsSame topicRetinal Development and DisordersFrench-language works237,207