Evidence map›Paper›PMID 41839984›Full record

ArticleScientific reports2026

Gene and cell line efficiency of CRISPR computed by tensor decomposition in genome-wide CRISPR-Cas9 knockout screens.

Y-H Taguchi, Turki Turki

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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

2 authors.

Y-H TaguchiDepartment of Physics, Chuo University, Tokyo, 112-8551, Japan, 1-13-27 Kasuga, Bunkyo-ku. tag@granular.com.
Turki TurkiDepartment of Computer Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia. tturki@kau.edu.sa.

Funding

King Abdulaziz University Deanship of Scientific Research, IPP: 70-611-2025
6 · The paper itself

Abstract

Genome-wide CRISPR-Cas9 knockout screens are often used to experimentally evaluate gene function. However, the efficacy of individual sgRNAs targeting unique genes varies and is difficult to integrate. In this study, tensor decomposition (TD) was used to integrate multiple sgRNAs and sgRNA profiles simultaneously. Thus, TD can discriminate between essential and non-essential genes with the performance comparative to that of Joint analysis of CRISPR/Cas9 knockout screens (JACKS), a type of SOTA that previously outperformed various other SOTA. In addition, although TD uses simple linear algebra, it can achieve good performance even without control samples, without which JACKS cannot be performed. Moreover, because raw and logarithmic values can achieve similar performances through TD for the largest dataset among the tested datasets, taking logarithmic values as has been done frequently, which is questioned. In conclusion, TD is the first method that can integrate multiple sgRNAs attributed to single a target and sgRNA profiles at the beginning simultaneously and can achieve a performance comparable to that of JACKS.

Indexed as

CRISPR-Cas SystemsGene Knockout TechniquesAlgorithmsCell LineHumansRNA, Guide, CRISPR-Cas SystemsRNA, Guide, CRISPR-Cas SystemsCRISPRGenome wide analysisNovel AI application in CRISPR-Cas9SgRNATensor decompositionUnsupervised learning

Identifiers

PMID41839984
PMCPMC13121791

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.