Evidence map›Paper›PMID 41896911›Full record

ArticleGenome biology2026

DeepISO: deep learning-powered prediction of protein-protein interaction rewiring generated by alternative splicing.

Xiaokun Guo, Linyang Jiang, Jiajun Li, Mengdi Yuan, Dianke Li, Wenyu Shi, Ziding Zhang, Stefan Wuchty

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Xiaokun Guo *State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
Linyang Jiang *State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
Jiajun LiState Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
Mengdi YuanState Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
Dianke LiState Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
Wenyu ShiState Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China. shiwy@cau.edu.cn.
Ziding ZhangState Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China. zidingzhang@cau.edu.cn.
Stefan WuchtyDepartment of Computer Science, University of Miami, Miami, FL, 33146, USA. wuchtys@cs.miami.edu.

Funding

National Natural Science Foundation of China 31970645
6 · The paper itself

Abstract

Isoforms from the same gene can significantly rewire protein interaction networks, but proteome-wide computational evaluation of these effects remains challenging. In this work, we present DeepISO, a deep learning framework for predicting isoform-specific interactions. DeepISO integrates two graph convolutional neural networks and a random forest model via a logistic regression model. To the best of our knowledge, this is the first approach to jointly leverage AlphaFold-predicted structures and ESM2 language model embeddings for this task. Compared with state-of-the-art PPI prediction tools, DeepISO demonstrates superior performance.

Indexed as

Alternative SplicingDeep LearningProtein Interaction MappingProtein Interaction MapsGraph Neural NetworksHumansPrediction AlgorithmsProtein IsoformsRandom ForestProtein IsoformsAlternative splicingDeep learningIsoformProtein–protein interactionStructure

Identifiers

PMID41896911
PMCPMC13147823

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.