Evidence map›Paper›PMID 34522290›Full record

ArticleComputational and structural biotechnology journal2021

MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation.

Yuexu Jiang, Duolin Wang, Yifu Yao, Holger Eubel, Patrick Künzler, Ian Max Møller, Dong Xu

Abstract read
In one paragraph

Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.

0numbers the graph read from it
0cells of the map it votes in
64citing 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

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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

64 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Subcellular localization as a driver of protein function.Nature reviews. Molecular cell biology · 2026
    Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026
    Review
  10. Article
  11. Article
  12. Article
  13. Structural dynamics of mitochondrial ATP synthase inbioRxiv : the preprint server for biology · 2025
    Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

4 more citing papers are in PubMed but not listed here.

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

7 authors.

Yuexu JiangDepartment of Electrical Engineering and Computer Science, Bond Life Sciences Center, Columbia, MO, USA.
Duolin WangDepartment of Electrical Engineering and Computer Science, Bond Life Sciences Center, Columbia, MO, USA.
Yifu YaoDepartment of Electrical Engineering and Computer Science, Bond Life Sciences Center, Columbia, MO, USA.
Holger EubelInstitute of Plant Genetics, Leibniz University Hannover, Hannover, Germany.
Patrick KünzlerInstitute of Plant Genetics, Leibniz University Hannover, Hannover, Germany.
Ian Max MøllerDepartment of Molecular Biology and Genetics, Aarhus University, Forsøgsvej 1, DK-4200 Slagelse, Denmark.
Dong XuDepartment of Electrical Engineering and Computer Science, Bond Life Sciences Center, Columbia, MO, USA.

Funding

Multi-view self-supervised deep learning for biological sequences and beyondR35GM126985 · NIGMS · UNIVERSITY OF SOUTH FLORIDA · PI DONG XU · 2018 to 2026
$3.8M
Deep learning for protein subcellular/sub-organelle localizations and localization motifsR21LM012790 · NLM · UNIVERSITY OF MISSOURI-COLUMBIA · PI XU, DONG · 2018 to 2019
$380k
NIGMS NIH HHS R35 GM126985NLM NIH HHS R21 LM012790
6 · The paper itself

Abstract

Prediction of protein localization plays an important role in understanding protein function and mechanisms. In this paper, we propose a general deep learning-based localization prediction framework, MULocDeep, which can predict multiple localizations of a protein at both subcellular and suborganellar levels. We collected a dataset with 44 suborganellar localization annotations in 10 major subcellular compartments-the most comprehensive suborganelle localization dataset to date. We also experimentally generated an independent dataset of mitochondrial proteins in

Indexed as

Deep learningExperimental benchmark datasetsMechanism studyProtein localizationWeb server

Identifiers

PMID34522290
PMCPMC8426535

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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.