ArticlePloS one2025
Use of hybrid quantum-classical algorithms for enhancing biomarker classification.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Quantum bioinformatics: a systematic review of methods, trends, and challenges.Briefings in bioinformatics · 2026Pooled it
- HIV-1 protease cleavage sites detection with a quantum convolutional neural network algorithm.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantum machine learning (QML) combines quantum computing with machine learning, offering potential for solving intricate problems. Our research delves into QML's application in identifying gene expression biomarkers for clear cell renal cell carcinoma (ccRCC) metastasis. ccRCC, the primary renal cancer subtype, poses significant challenges due to its high lethality and complex metastasis process. Despite extensive research, understanding the mechanisms of cancer cell dissemination and establishment in distant sites remains elusive. Identifying metastasis biomarkers is a daunting task in machine learning. Our study addresses the need for improved execution time and accuracy in QSVC and QNN algorithms compared to SVC and NN for binary classification. Drawing inspiration from the Neural Quantum Embedding (NQE) method, we propose a two-stage approach for the binary classification problem. We aim to assess if integrating NQE with QSVC/QNN enhances performance compared to NQE with SVC/NN across diverse biomedical datasets, demonstrating the effectiveness and generalizability of the approach.
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Registered trials
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 Socratic graph.