Evidence map›Paper›PMID 41210308›Full record

ArticleComputational and structural biotechnology journal2025

Quantum neural networks meet federated learning for DNA mutation prediction.

Sotirios Messinis, Ioannis Rallis, Nikolaos Temenos, Dimitrios Kalogeras, George A Kastis, Nikolaos Doulamis, Nicholas E Protonotarios

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2025. 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. Review
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.

Sotirios MessinisInstitute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, 15780, Greece.
Ioannis RallisInstitute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, 15780, Greece.
Nikolaos TemenosInstitute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, 15780, Greece.
Dimitrios KalogerasInstitute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, 15780, Greece.
George A KastisMathematics Research Center, Academy of Athens, Athens, 11527, Greece.
Nikolaos DoulamisInstitute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, 15780, Greece.
Nicholas E ProtonotariosMathematics Research Center, Academy of Athens, Athens, 11527, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this work, we introduce QuanGAT, a hybrid framework integrating quantum neural networks (QNNs), graph attention networks (GATs), and federated learning to tackle DNA mutation prediction in biomedical graphs in a privacy-preserving, noise-aware setting. Motivated by quantum biology's potential to describe mutation-level mechanisms, our approach is able to simulate decentralized genomic environments in order to protect sensitive data, while capturing complex biological variability. QuanGAT employs a QNN encoder based on parameterized quantum circuits, incorporating quantum noise, via the so-called

Indexed as

Biomedical graphsDNA mutation predictionFederated learningGraph attention networksQuantum neural networks

Identifiers

PMID41210308
PMCPMC12593009

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.