QuanAnts Machine: A Quantum Algorithm for Biomarker Discovery
The discovery of biomarker sets for a targeted pathway is a challenging problem in biomedical medicine, which is computationally prohibited on classical algorithms due to the massive search space. Here, I present a quantum algorithm named QuantAnts Machine to address the task. The proposed algorithm is a quantum analog of the classical Ant Colony Optimization (ACO). We create the mixture of multi-domain from genetic networks by representation theory, enabling the search of biomarkers from the multi-modality of the human genome. Although the proposed model can be generalized, we investigate the RAS-mutational activation in this work. To the end, QuantAnts Machine discovers rarely-known biomarkers in clinical-associated domain for RAS-activation pathway, including COL5A1, COL5A2, CCT5, MTSS1 and NCAPD2. Besides, the model also suggests several therapeutic-targets such as JUP, CD9, CD34 and CD74.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
How quantum computing can enhance biomarker discovery
Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very early indicators of a change in health …
Time SeriesBiomarker Discovery with Quantum Neural Networks: A Case-study in CTLA4-Activation Pathways
Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery tasks.…
Quantum AI for Cancer Diagnostic Biomarker Discovery
Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer classification, biomarker discovery, and bioinformatics diagnostics. In thi…
Quantum Machine LearningCancer ClassificationCan a Quantum Support Vector Machine algorithm be utilized to identify Key Biomarkers from Multi-Omics data of COVID19 patients?
Identifying key biomarkers for COVID-19 from high-dimensional multi-omics data is critical for advancing both diagnostic and pathogenesis research. In this study, we evaluated the applicability of the Quantum Support Vec…
DiagnosticregressionQuantum machine learning framework for longitudinal biomedical studies
Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders such as cancer and neurodegenerative di…
Quantum Machine Learning