Evidence map›Paper›PMID 39091825›Full record

ArticlebioRxiv : the preprint server for biology2024

Prediction of protein subcellular localization in single cells.

Xinyi Zhang, Yitong Tseo, Yunhao Bai, Fei Chen, Caroline Uhler

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

5 · Who and what money

Authors and funding

5 authors.

Xinyi ZhangDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, U.S.A.ORCID 0000-0003-4996-4698
Yitong TseoComputational and Systems Biology Program, Massachusetts Institute of Technology, U.S.A.
Yunhao BaiBroad Institute of MIT and Harvard, U.S.A.
Fei ChenBroad Institute of MIT and Harvard, U.S.A.ORCID 0000-0003-2308-3649
Caroline UhlerDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, U.S.A.ORCID 0000-0002-7008-0216

Funding

COVID19 Slide-seqR01HG010647 · NHGRI · BROAD INSTITUTE, INC. · PI Fei Chen, Evan Z Macosko · 2019 to 2026
$6.0M
Causal Representation Learning for the Spatial Analysis of Transcriptomic and Imaging Data in Tissue ContextsDP2AT012345 · NCCIH · BROAD INSTITUTE, INC. · PI UHLER, CAROLINE · 2022 to 2025
$2.3M
NCCIH NIH HHS DP2 AT012345NHGRI NIH HHS R01 HG010647
6 · The paper itself

Abstract

The subcellular localization of a protein is important for its function and interaction with other molecules, and its mislocalization is linked to numerous diseases. While atlas-scale efforts have been made to profile protein localization across various cell lines, existing datasets only contain limited pairs of proteins and cell lines which do not cover all human proteins. We present a method that uses both protein sequences and cellular landmark images to perform

Identifiers

PMID39091825
PMCPMC11291118

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.