Evidence map›Paper›PMID 41379033›Full record

ArticleBiometrics2025

Large row-constrained supersaturated designs for high-throughput screening.

Byran J Smucker, Stephen E Wright, Isaac Williams, Richard C Page, Andor J Kiss, Surendra Bikram Silwal, Maria Weese, David J Edwards

Abstract read
In one paragraph

Article in Biometrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Byran J SmuckerDepartment of Statistics, Miami University, Oxford, OH 45056, United States.ORCID 0000-0003-4076-8973
Stephen E WrightDepartment of Statistics, Miami University, Oxford, OH 45056, United States.
Isaac WilliamsDepartment of Statistics, Miami University, Oxford, OH 45056, United States.
Richard C PageDepartment of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, United States.
Andor J KissCenter for Bioinformatics and Functional Genomics, Miami University, Oxford, OH 45056, United States.
Surendra Bikram SilwalDepartment of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, United States.
Maria WeeseDepartment of Information Systems & Analytics, Miami University, Oxford, OH 45056, United States.
David J EdwardsDepartment of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA 23284, United States.

Funding

Triage mechanisms for directing protein refolding and degradationR35GM128595 · NIGMS · MIAMI UNIVERSITY OXFORD · PI Richard C Page · 2018 to 2026
$2.8M
BJSNIGMS NIH HHS R35 GM128595NIH HHS GM128595
6 · The paper itself

Abstract

High-throughput screening, in which large numbers of compounds are traditionally studied one-at-a-time in multiwell plates against specific targets, is widely used across many areas of the biological sciences, including drug discovery. To improve the effectiveness of these screens, we propose a new class of supersaturated designs that guide the construction of pools of compounds in each well. Because the size of the pools is typically limited by the particular application, the new designs accommodate this constraint and are part of a larger procedure that we call Constrained Row Screening or CRowS. We develop an efficient computational procedure to construct the CRowS designs, provide some initial lower bounds on the average squared off-diagonal values of their main-effects information matrix, and study the impact of the constraint on design quality. We also show via simulation that CRowS is statistically superior to the traditional one-compound-one-well approach as well as an existing pooling method, and demonstrate the use of the new methodology on a Verona Integron-encoded Metallo-$\beta$-lactamase-2 assay.

Indexed as

Drug DiscoveryHigh-Throughput Screening Assaysbeta-LactamasesBiometryComputer SimulationModels, Statisticalbeta-Lactamasesbiological screeningexperimental designthe Lasso

Identifiers

PMID41379033
PMCPMC12696866

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.