Evidence map›Paper›PMID 31243057›Full record

ArticleGenetics2019

Matrix Linear Models for High-Throughput Chemical Genetic Screens.

Jane W Liang, Robert J Nichols, Śaunak Sen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

3 authors.

Jane W LiangDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115.ORCID 0000-0002-2302-3809
Robert J NicholsDepartment of Microbiology and Immunology, University of California, San Francisco, California 94143.
Śaunak SenDepartment of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee 38163 sen@uthsc.edu.ORCID 0000-0003-4519-6361

Funding

Systems Genetic Analysis of Multi-parent CrossesR01GM070683 · NIGMS · JACKSON LABORATORY · PI BROMAN, KARL W, CHURCHILL, GARY A · 2004 to 2022
$6.8M
TIM Project NIDA P30 CenterP30DA044223 · NIDA · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI SABA, LAURA MAREN, WILLIAMS, ROBERT W. · 2017 to 2021
$3.9M
The UCSF Pregnancy Exposures to Environmental Chemicals (PEEC) Children's CenterP01ES022841 · NIEHS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI WOODRUFF, TRACEY J. · 2013 to 2018
$3.9M
A Unified High Performance Web Service for Systems Genetics and Precision MedicineR01GM123489 · NIGMS · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI PRINS, PJOTR, SEN, SAUNAK · 2017 to 2024
$3.7M
Statistical Methods for Experimental Genome PopulationsR01GM078338 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SEN, SAUNAK · 2007 to 2011
$1.1M
NIDA NIH HHS P30 DA044223NIEHS NIH HHS P01 ES022841NIGMS NIH HHS R01 GM070683NIGMS NIH HHS R01 GM078338NIGMS NIH HHS R01 GM123489
6 · The paper itself

Abstract

We develop a flexible and computationally efficient approach for analyzing high-throughput chemical genetic screens. In such screens, a library of genetic mutants is phenotyped in a large number of stresses. Typically, interactions between genes and stresses are detected by grouping the mutants and stresses into categories, and performing modified

Indexed as

Models, GeneticMutagenesisAnti-Bacterial AgentsEscherichia coliGene-Environment InteractionLinear ModelsReverse GeneticsSoftwareAnti-Bacterial Agentschemical genetic screensE. colihigh-throughput datalinear models

Identifiers

PMID31243057
PMCPMC6707451

What OpenQuestion holds

Textmetadata
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.